Neural tumor image analysis and identification method

Abstract

The invention relates to the technical field of image recognition, in particular to a neural tumor image analysis and recognition method, which comprises the following steps of: based on an Image Net image data set, performing pre-training of a learning model by adopting a convolutional neural network, obtaining feature recognition capability, performing feature extraction, and generating a model after basic training. According to the method, meta learning, transfer learning, a conditional generative adversarial network, a medical image registration method based on deep learning, synchronous theoretical analysis and a distributed calculation framework are comprehensively utilized, so that the neural tumor image analysis and recognition performance is remarkably improved; and particularly, the method has obvious advantages in the aspects of processing few samples, rare tumor types and multi-modal image data. The deep learning model is pre-trained on a large-scale general image data set, and fine tuning is performed in combination with a meta-learning algorithm, so that the adaptability and learning efficiency of the model to few sample data are significantly improved, and the problem of insufficient accuracy during processing of rare or special phenotype neural tumor images is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and in particular to a method for analyzing and recognizing neural tumor images. Background Art

[0002] The field of image recognition technology is a technology that uses computer vision and machine learning algorithms to automatically understand and interpret image data. In medical image analysis, image recognition technology focuses specifically on identifying and classifying pathological features from complex medical images, such as tumors or other abnormal structures in CT scans and MRI scans. This technology extracts and analyzes feature information in images, enabling computers to automatically identify specific content in images, thereby supporting medical professionals in diagnosis and treatment decision-making.

[0003] The neuro-tumor image analysis and recognition method is an image processing technology specifically used to detect, identify and analyze nervous system tumors (such as brain tumors). Its purpose is to improve the accuracy and efficiency of neuro-tumor detection through automated means, reduce dependence on radiologists and pathologists, and thus play a key role in the early detection and diagnosis of tumors. By accurately identifying the location, size and type of the tumor, the method aims to help medical professionals develop more effective treatment plans, thereby improving patient treatment outcomes and survival rates.

[0004] Although the existing technology has made some progress in the automatic recognition and classification of neural tumor images, it is difficult to accurately recognize rare or special phenotype neural tumor images in terms of learning efficiency and adaptability when dealing with very few samples, and it is not adaptable enough to complex and variable image features. In terms of data enhancement, the conditional generative adversarial network has not been fully utilized to simulate rare tumor conditions, which limits the diversity and richness of the training data set, thereby affecting the generalization ability of the model. In addition, in terms of precise alignment and analysis of multimodal images, it is difficult to effectively integrate and analyze image data from different time points and devices, resulting in inaccurate monitoring and quantification of tumor development dynamics. In the analysis of coordinated changes in images, there is a lack of effective theoretical and algorithmic frameworks to explore and reveal the interactions between the tumor and the surrounding tissues, which limits the possibility of a deep understanding of tumor behavior. For content-based image retrieval systems, the existing technology still needs to be improved in terms of deep learning algorithm optimization and efficient similarity measurement strategies, which affects retrieval efficiency and accuracy. Finally, in terms of large-scale image data processing, the existing computing framework fails to effectively cope with the processing requirements of high-resolution images and large data sets, and faces challenges in data storage, parallel processing, and load balancing mechanisms, which affects the speed and efficiency of image analysis. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method for analyzing and identifying neural tumor images.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for analyzing and identifying neural tumor images, comprising the following steps:

[0007] S1: Based on the ImageNet image dataset, a convolutional neural network is used to pre-train the learning model, obtain feature recognition capabilities, and perform feature extraction to generate a basic trained model;

[0008] S2: Based on the basic trained model, a model-independent meta-learning algorithm is used to perform refinement and adjustment, so that the model matches the neural tumor image data of scarce samples, and the model performance is optimized to generate an adjusted model;

[0009] S3: Based on the adjusted model, using a conditional generative adversarial network, inputting a specified tumor type condition, acquiring rare or new neural tumor image data, and generating a simulated extended image dataset;

[0010] S4: Based on the simulated extended image data set, a feature matching algorithm is used to perform image retrieval, an image matching the query image is captured from the data set, and a matching image retrieval result set is generated;

[0011] S5: Based on the matching image retrieval result set, a deep learning-based medical image registration method is used to automatically align the images, track the development and changes of the tumor, and compare the time points to generate a registered image sequence;

[0012] S6: Based on the registered image sequence, applying a synchronization theory analysis method, quantifying the degree of synchronization between various regions in the image, revealing the coordinated behavior of tumor cells, and generating a synchronization analysis result of a characteristic image;

[0013] S7: Based on the simulated extended image data set, the matching image retrieval result set, the registered image sequence and the synchronous analysis result of the feature image, a distributed computing framework is used to perform parallel processing and analysis, and data fusion is performed to generate a data distributed processing result;

[0014] S8: Based on the distributed data processing results and the synchronous analysis results of the feature images, a machine learning algorithm is used to perform neural tumor image recognition and analysis again to optimize recognition accuracy and efficiency and generate image recognition optimization results.

[0015] The present invention has the following improvements: the model after basic training includes a network structure after feature layer extraction capability, classification accuracy and adaptability are optimized; the adjusted model includes network parameters for optimizing the characteristics of specified neural tumor images, a learning strategy for matching data-scarce environments, and a loss function for optimizing diagnostic accuracy; the simulated and extended image data set includes a diverse tumor feature simulation, images of various tumor progression stages, and tumor images under variable backgrounds; the matched image retrieval result set includes an image list matched according to image content similarity, image features selected based on similarity scores, and index tags for retrieval; the registered image sequence includes tumor change images arranged in time series and automatically aligned, alignment parameters for analyzing tumor progression, and dynamic features for change tracking; the synchronous analysis results of the feature images include a phase consistency index that reveals the coordinated behavior of tumor cells, a dynamic change graph of tumor growth synchronization, and quantitative analysis information of regional interactions; the data distributed processing results include data aggregation indicators obtained through parallel computing, optimized computing resource allocation suggestions, and data processing performance evaluation information; the image recognition optimization results include a recognition accuracy index after reanalysis, reduced misjudgment rate statistics, and optimized diagnostic efficiency for specified tumor types.

[0016] The present invention has been improved in that, based on the ImageNet image data set, a convolutional neural network is used to pre-train the learning model, obtain feature recognition capabilities, and perform feature extraction. The specific steps of generating the model after basic training are as follows:

[0017] S101: Based on the ImageNet image dataset, use the Resize method in the torchvision.transforms library to resize the image to 224x224 pixels, the Normalize method to scale the image pixel values ​​to between 0 and 1, and the RandomHorizontalFlip method to perform random horizontal flipping on the image to generate a preprocessed image dataset;

[0018] S102: Based on the preprocessed image data set, a hierarchical structure design of a convolutional neural network is performed, the network is constructed using the PyTorch framework, multiple Conv2D layers and MaxPooling2D layers are added to alternately perform feature extraction, ReLU is used as an activation function for nonlinear processing, and a BatchNormalization layer is used to standardize the input of each layer of the network to generate a model architecture design result;

[0019] S103: Based on the model architecture design results, model training is performed, using the Adam optimizer, setting the learning rate to 0.001, using the CrossEntropyLoss loss function to handle multi-classification problems, and configuring using the associated functions in the torch.optim and torch.nn libraries. By iteratively optimizing the network parameters, a basic trained model is generated.

[0020] The present invention has the following improvements: based on the model after basic training, a model-independent meta-learning algorithm is used to perform refinement and adjustment, so that the model matches the neural tumor image data of scarce samples and optimizes the model performance. The specific steps of generating the adjusted model are as follows;

[0021] S201: Based on the basic trained model, select neural tumor image data, resize the image to 224x224 pixels using Python's PIL library, normalize the pixel values ​​to a range of 0 to 1 using the numpy library, and generate a selected data set;

[0022] S202: Based on the selected data set, a model-independent meta-learning algorithm is adopted, and the PyTorch framework in Python is used to set the learning rate of the inner loop update to 0.01, and the outer loop learning rate is adjusted to 0.001. Through two-stage training, the parameters of each batch task are fine-tuned in the inner loop, and the global parameters are adjusted in the outer loop to generate a meta-learning matching model;

[0023] S203: Based on the meta-learning matching model, the model is refined and adjusted, and the parameters are optimized using the Adam optimizer in torch.optim. A cross-validation process is set up to evaluate the performance of the model on various neuro-tumor imaging tasks, and the number of training rounds and learning rate are adjusted according to performance feedback to generate an adjusted model.

[0024] The present invention is improved in that, based on the adjusted model, through a conditional generative adversarial network, inputting a specified tumor type condition, obtaining rare or new neural tumor image data, and generating a simulated extended image data set are performed in the following specific steps;

[0025] S301: Based on the adjusted model, using Python's PyTorch framework, a deep convolutional network structure is configured for the generator, the input layer receives the tumor type label and random noise as conditional input, the discriminator is set to a convolutional network, the authenticity of the input image is judged, and the basic cGAN architecture design result is generated;

[0026] S302: Based on the design result of the basic cGAN architecture, network training is implemented, using the Adam optimizer, setting the learning rate to 0.0002, the batch size to 64, and the cross entropy as the loss function. The generator and the discriminator are alternately trained, and through the training loop in the PyTorch framework, the conditional input is controlled so that the generator can generate matching images according to the specified tumor type, and the discriminator is optimized to identify the generated image and the real image, so as to generate a trained cGAN model;

[0027] S303: Based on the trained cGAN model, setting the conditional label of the specified tumor type, and using the internal conditional embedding and generator network of the model to generate new neural tumor images, including adjusting the generator input conditions, generating images according to the expected tumor type conditions, and generating a simulated extended image dataset.

[0028] The present invention is improved in that, based on the simulated extended image data set, a feature matching algorithm is used to perform image retrieval, an image matching the query image is captured from the data set, and the specific steps of generating a matching image retrieval result set are as follows;

[0029] S401: Based on the simulated extended image dataset, a pre-trained VGG16 network is used to directly extract image features through its convolution layer, the pre-trained model is loaded through tf.keras.applications.VGG16 of the TensorFlow framework, and the image dataset is processed using the predict method, each image is converted into a feature vector of a fixed length, key visual information is captured, and an image feature vector set is generated;

[0030] S402: Based on the image feature vector set, a cosine similarity algorithm is applied to perform feature matching. In the Python environment, the cosine value between two feature vectors is calculated by numpy.dot and numpy.linalg.norm, and the similarity is determined. The feature vectors of each pair of images in the data set are iteratively compared to calculate their similarity and generate a similarity score list.

[0031] S403: Based on the similarity score list, perform image retrieval, filter and query according to the similarity score, use the sorting function of the Python list, sort all images in descending order based on the similarity score through the sorted function, select the image with the highest similarity, and generate a matching image retrieval result set.

[0032] The present invention has the following improvements: based on the matching image retrieval result set, a deep learning-based medical image registration method is used to automatically align images, track the development and changes of tumors, and compare time points. The specific steps of generating a registered image sequence are as follows:

[0033] S501: Based on the matching image retrieval result set, deploy a spatial transformer network as an image registration method, using a PyTorch framework, and configuring a positioning network for learning parameters of image-to-image transformation. The positioning network is composed of a convolutional layer and a fully connected layer, and outputs spatial transformation parameters from a target image to a source image, including rotation, translation, and scaling, to generate an image alignment parameter configuration;

[0034] S502: Based on the image alignment parameter configuration, perform an image alignment operation, use the spatial transformation parameters output by the spatial transformer network, obtain a transformed grid through torch.nn.functional.affine_grid, use torch.nn.functional.grid_sample to apply the grid to the source image, perform automatic image alignment, and generate an aligned image set;

[0035] S503: Based on the aligned image set, a differential image analysis method is used to compare the images in the time series. By calculating the pixel-level differences between images at consecutive time points, the cv2.absdiff function of OpenCV is used to calculate the image differences, revealing the size and morphological changes of the tumor at multiple time points, and generating a registered image sequence.

[0036] The present invention is improved in that, based on the registered image sequence, a synchronization theory analysis method is applied to quantify the synchronization degree between various regions in the image and reveal the coordinated behavior of tumor cells, and the specific steps of generating the synchronization analysis result of the characteristic image are as follows;

[0037] S601: Based on the registered image sequence, perform time series analysis to extract the frequency characteristics of each region in the image that change with time, use fast Fourier transform, call numpy.fft through Python's NumPy library, and convert the time series of each pixel point with the fft function to generate a frequency spectrum for each region and generate a time-frequency feature set;

[0038] S602: Based on the time-frequency feature set, a phase locking value algorithm is applied to evaluate the degree of synchronization between multiple regions in the image, and the instantaneous phase of the signal is obtained by calculating the consistency of the phase difference in the time-frequency spectrum of the multiple regions and performing Hilbert transform using the scipy.signal.hilbert function. This can quantify the phase synchronization between each region in the image sequence, reveal the degree of synchronization between regions, and generate regional synchronization analysis results;

[0039] S603: Based on the regional synchronization analysis results, a synchronization network diagram of the coordinated behavior of tumor cells is drawn. The network diagram is visualized using Python's Matplotlib library to show the intensity of synchronization between multiple regions in the image sequence, where the network nodes represent the regions in the image, and the intensity of the edges represents the size of the synchronization between the regions, revealing the coordinated change pattern between tumor cells and generating synchronization analysis results of the characteristic image.

[0040] The present invention has the following improvements: based on the simulated extended image data set, the matching image retrieval result set, the registered image sequence and the synchronous analysis result of the feature image, a distributed computing framework is used to perform parallel processing and analysis, and data fusion is performed to generate the data distributed processing results;

[0041] S701: Based on the simulated extended image dataset, the matching image retrieval result set and the registered image sequence, configure the Apache Spark environment to load data, load the image data through the spark.read.format(image) method of Spark, perform initial parallel reading and format conversion on the image dataset, and generate a uniformly loaded dataset;

[0042] S702: Based on the synchronous analysis results of the uniformly loaded data sets and feature images, using the distributed computing capability of Spark, applying a specified processing flow to each data set, including using the algorithm in Spark MLlib to perform feature extraction on the simulated extended image data set, applying an image alignment algorithm to the matching image retrieval result set, and fusing the registered image sequence with the synchronous analysis results of the feature images, executing tasks in parallel through customized Spark conversion operations and actions, and generating intermediate results of the fusion processing;

[0043] S703: Based on the intermediate results of the fusion processing, a distributed computing framework is used to perform data fusion and analysis, and the processing results of all data sets are comprehensively referred to. Spark's reduceByKey aggregation operation is used to summarize the data, and aggregation analysis is performed on multiple types of image data sets to generate data distributed processing results.

[0044] The present invention has the following improvements: based on the data distributed processing results and the synchronous analysis results of the characteristic images, a machine learning algorithm is used to perform neural tumor image recognition and analysis again to optimize the recognition accuracy and efficiency. The specific steps of generating the image recognition optimization results are as follows:

[0045] S801: Based on the data distributed processing results and the synchronous analysis results of the feature images, prepare a data set for machine learning training, integrate the features of the data, and use feature selection technology to screen neural tumor image recognition features based on contribution, including selecting K features through the SelectKBest method in the Scikit-learn library of Python, optimizing the subsequent machine learning model training process, and generating an optimized training data set;

[0046] S802: Based on the optimized training data set, a convolutional neural network model in deep learning is used for training. The configuration of the model includes setting a network hierarchy, an activation function, and an optimizer, including using the Keras library to build the model, setting an Adam optimizer, and selecting categorical_crossentropy as the loss function. The training process of the model is executed so that it can learn the patterns and features in the data and generate a trained CNN model.

[0047] S803: Based on the trained CNN model, the neural tumor images are identified and analyzed, the model is used to predict the test data set, the recognition accuracy and efficiency of the model are evaluated, the accuracy, recall rate and F1 score performance indicators of the model on the test set are calculated, and the classification_report method in the Scikit-learn library is used to generate model performance information and generate image recognition optimization results.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are:

[0049] In the present invention, by comprehensively using meta-learning, transfer learning, conditional generative adversarial networks, medical image registration methods based on deep learning, synchronous theoretical analysis and distributed computing frameworks, the performance of neural tumor image analysis and recognition is significantly improved, especially in processing few samples, rare tumor types and multimodal image data. By pre-training the deep learning model on a large-scale general image data set and fine-tuning it in combination with the meta-learning algorithm, the adaptability and learning efficiency of the model to few sample data are significantly improved, and the problem of insufficient accuracy of traditional methods in processing rare or special phenotype neural tumor images is effectively solved. By using cGANs for targeted data enhancement, high-quality and diverse simulated tumor images can be generated, enriching the training data set and improving the generalization ability of the model. In addition, the medical image registration method based on deep learning is adopted to achieve accurate alignment and analysis of multimodal images generated at different time points and devices, providing more accurate quantitative data for monitoring the development dynamics of tumors. By applying the synchronous theoretical analysis method, the coordinated change pattern between tumor cells can be revealed, providing a new perspective for understanding tumor behavior and development mechanisms. The optimization of the content-based image retrieval system, combined with efficient feature extraction and similarity measurement strategies, significantly improved the retrieval efficiency and accuracy, providing doctors with a fast and accurate diagnostic reference. Finally, by adopting a distributed computing framework, efficient processing and analysis of large-scale neuro-oncology image datasets was achieved, effectively responding to the processing needs of high-resolution images and large datasets, and significantly improving the speed and efficiency of image analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flowchart of a method for analyzing and identifying neural tumor images is proposed for the present invention;

[0051] Figure 2 A schematic diagram of the detailed flow of step S1 in a method for analyzing and identifying neural tumor images is proposed in the present invention;

[0052] Figure 3 A schematic diagram of the detailed process of step S2 in a method for analyzing and identifying neural tumor images is proposed in the present invention;

[0053] Figure 4 A schematic diagram of the detailed process of step S3 in a method for analyzing and identifying neural tumor images is proposed in the present invention;

[0054] Figure 5 A schematic diagram of a detailed flow chart of step S4 in a method for analyzing and identifying neural tumor images is proposed in the present invention;

[0055] Figure 6 The present invention proposes a schematic diagram of the detailed process of step S5 in a method for analyzing and identifying neural tumor images;

[0056] Figure 7 The present invention proposes a schematic diagram of the detailed process of step S6 in a method for analyzing and identifying neural tumor images;

[0057] Figure 8 The present invention proposes a detailed flow chart of step S7 in a method for analyzing and identifying neural tumor images;

[0058] Fig. 9 The present invention proposes a detailed flowchart of step S8 in a method for analyzing and identifying neural tumor images. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0061] Example

[0062] See also Figure 1 The present invention provides a technical solution: a method for analyzing and identifying neural tumor images, comprising the following steps:

[0063] S1: Based on the ImageNet image dataset, a convolutional neural network is used to pre-train the learning model, obtain feature recognition capabilities, and perform feature extraction to generate a basic trained model;

[0064] S2: Based on the basic trained model, a model-independent meta-learning algorithm is used to perform refinement and adjustment to match the scarce sample neural tumor image data, optimize the model performance, and generate an adjusted model;

[0065] S3: Based on the adjusted model, a conditional generative adversarial network is used to input the specified tumor type conditions, obtain rare or new neural tumor image data, and generate a simulated extended image dataset;

[0066] S4: Based on the simulated extended image dataset, a feature matching algorithm is used to perform image retrieval, images matching the query image are captured from the dataset, and a matching image retrieval result set is generated;

[0067] S5: Based on the matching image retrieval result set, a deep learning-based medical image registration method is used to automatically align the images, track the development and changes of the tumor, and compare the time points to generate a registered image sequence;

[0068] S6: Based on the registered image sequence, the synchronization theory analysis method is applied to quantify the synchronization degree between various regions in the image, reveal the coordinated behavior of tumor cells, and generate the synchronization analysis results of the characteristic image;

[0069] S7: Based on the simulated extended image dataset, the matching image retrieval result set, the registered image sequence and the synchronous analysis results of the feature image, a distributed computing framework is used to perform parallel processing and analysis, and data fusion is performed to generate data distributed processing results;

[0070] S8: Based on the results of distributed data processing and the synchronous analysis results of feature images, a machine learning algorithm is used to perform neuro-tumor image recognition and analysis again to optimize recognition accuracy and efficiency and generate image recognition optimization results.

[0071] The model after basic training includes the network structure after feature layer extraction ability, classification accuracy and adaptability optimization. The adjusted model includes network parameters optimized for specified neural tumor image characteristics, learning strategies matching data-scarce environments, and loss functions optimizing diagnostic accuracy. The simulated and expanded image data set includes diversified tumor feature simulations, images of multiple tumor progression stages, and tumor images under variable backgrounds. The matched image retrieval result set includes a list of images matched according to image content similarity, image features selected based on similarity scores, and index tags for retrieval. The registered image sequence includes tumor change images arranged in time series and automatically aligned, alignment parameters for analyzing tumor progression, and dynamic features for change tracking. The synchronous analysis results of feature images include phase consistency indicators that reveal the collaborative behavior of tumor cells, dynamic change maps of tumor growth synchronization, and quantitative analysis information of regional interactions. The data distributed processing results include data aggregation indicators obtained through parallel computing, optimized computing resource allocation recommendations, and data processing performance evaluation information. The image recognition optimization results include re-analyzed recognition accuracy indicators, reduced misjudgment rate statistics, and optimized diagnostic efficiency for specified tumor types.

[0072] In step S1, a convolutional neural network is used to pre-train the learning model on the ImageNet image dataset. The process involves putting each image in the image dataset through a series of pre-processing operations, such as resizing the image to a uniform resolution of 224x224 pixels, normalizing the image pixel values ​​to between 0 and 1, and applying random horizontal flipping to increase data diversity. Subsequently, a convolutional neural network model is constructed, which contains a structure with multiple convolutional layers and pooling layers alternating to extract feature information of the image. The activation function uses a linear rectifier function (ReLU) for nonlinear transformation, and a batch normalization layer is used to accelerate the training process and improve the generalization ability of the model. During the training process, the Adam optimizer and the cross entropy loss function are used, and the learning rate is set to 0.001. The parameters of the convolutional neural network are optimized through iteration. A basic post-training model with feature recognition capabilities is generated, which provides a strong feature extraction foundation for the subsequent steps.

[0073] In step S2, based on the basic trained model, the model-independent meta-learning algorithm is used to refine the model. In this process, the neural tumor image data is selected as the target task dataset, each image is resized to 224x224 pixels, and the pixel value is normalized to the range of 0 to 1. Using the model-independent meta-learning algorithm, the inner loop learning rate is set to 0.01 and the outer loop learning rate is set to 0.001. A two-stage training strategy is adopted: fast adaptive fine-tuning of each batch task in the inner loop, and adjustment of the model global parameters in the outer loop, so that the model can better adapt to the neural tumor image data of scarce samples. The generated adjusted model has higher diagnostic accuracy and adaptability to scarce environments.

[0074] In step S3, a simulated extended image dataset is generated using a conditional generative adversarial network. During the process, the generator is configured as a deep convolutional network structure, and the input layer receives tumor type labels and random noise as conditional inputs, while the discriminator is also set to a convolutional network structure, which is responsible for judging the authenticity of the input image. Through the designed network training process, the Adam optimizer is used, the learning rate is set to 0.0002 and the batch size is 64, the loss function uses cross entropy, and the alternating training of the generator and the discriminator is implemented. Through the control of conditional input, the generator can generate matching images according to the specified tumor type conditions. The generated simulated extended image dataset contains a variety of tumor feature simulations and tumor images under changing backgrounds, providing rich and diverse data resources for model training.

[0075] In step S4, image retrieval is performed using a feature matching algorithm based on the simulated extended image dataset. This process mainly involves using a pre-trained deep network such as VGG16 to extract feature vectors from the image, and applying the cosine similarity algorithm to calculate the similarity between the query image and each image in the dataset. By comparing the similarity between feature vectors, the image that best matches the query image is selected to generate a matching image retrieval result set. The feature extraction capability of the deep learning model and the efficient similarity measurement method are effectively utilized to provide an accurate candidate image set for subsequent image analysis and diagnosis.

[0076] In step S5, the matched images are automatically aligned using a deep learning-based medical image registration method. Image registration is the process of aligning the same scenes or objects in different images, which is crucial for tracking the development and changes of tumors. The optimal alignment parameters between images, including rotation, translation, and scaling, are automatically learned by using deep learning methods such as spatial transformation networks. In this way, even images from different time points, different angles, or different devices can be accurately aligned, allowing doctors to accurately track the changes in the tumor and provide important support for clinical diagnosis and treatment decisions.

[0077] In step S6, the degree of synchronization between various regions in the image is quantified through the synchronization theory analysis method, and the coordinated behavior of tumor cells is revealed. By using time series analysis techniques such as fast Fourier transform, the pixel intensity of each region in the image that changes over time is converted into the frequency domain, and the degree of synchronization between different regions is quantified by calculating the phase consistency between different regions. This analysis reveals the interaction and coordinated change patterns within the tumor and between the tumor and surrounding tissues, providing a new perspective for understanding the growth and spread mechanism of tumors.

[0078] In step S7, parallel processing and analysis are performed through a distributed computing framework, and data fusion is performed. By integrating multiple source data sets such as simulated extended image data sets, matching image retrieval result sets, and registered image sequences, distributed computing frameworks such as Apache Spark are used for efficient processing. This includes batch feature extraction, image alignment, and synchronous analysis of images. Distributed computing not only significantly improves the speed of data processing, but also can process large-scale data sets, ensuring the efficiency and accuracy of the analysis process.

[0079] In step S8, based on the results of data distributed processing and the synchronous analysis results of feature images, machine learning algorithms are used to optimize the recognition and analysis of neural tumor images. By using deep learning models such as convolutional neural networks to learn and classify features, the accuracy and efficiency of recognition are improved through refined feature selection and model training processes. In addition, the model is evaluated through test data sets, model parameters are optimized, and the misrecognition rate is reduced. The resulting image recognition optimization results provide doctors with more accurate diagnostic information, which helps to improve the success rate of treatment.

[0080] See also Figure 2 ,Based on the ImageNet image dataset, a convolutional neural network is used to pre-train the learning model,obtain feature recognition capabilities, and perform feature extraction.,The specific steps for generating the model after basic training are as follows;

[0081] S101: Based on the ImageNet image dataset, use the Resize method in the torchvision.transforms library to resize the image to 224x224 pixels, the Normalize method to scale the image pixel values ​​to between 0 and 1, and the RandomHorizontalFlip method to perform random horizontal flipping on the image to generate a preprocessed image dataset;

[0082] S102: Based on the preprocessed image dataset, the hierarchical structure of the convolutional neural network is designed. The network is built using the PyTorch framework. Multiple Conv2D layers and MaxPooling2D layers are added to perform feature extraction alternately. ReLU is used as the activation function for nonlinear processing. The BatchNormalization layer is used to standardize the input of each layer of the network to generate the model architecture design results.

[0083] S103: Based on the model architecture design results, the model is trained using the Adam optimizer, the learning rate is set to 0.001, the loss function uses CrossEntropyLoss to handle multi-classification problems, and the associated functions in the torch.optim and torch.nn libraries are configured. The network parameters are iteratively optimized to generate a basic trained model.

[0084] In sub-step S101, the efficiency and effect of model training are improved through image preprocessing. The ImageNet image dataset is used, which contains high-resolution images of various categories. The size of each image is uniformly adjusted to 224x224 pixels using the Resize method of the torchvision.transforms library, because the convolutional neural network requires the input image size to be fixed, so as to ensure that the parameters and operations in the network can be correctly matched. The Normalize method is used to scale the pixel values ​​of the image to a range between 0 and 1, which helps the model converge faster, because a smaller numerical range can reduce numerical instability during model training. The RandomHorizontalFlip method is applied to randomly flip the image horizontally to increase the diversity of the dataset so that the model will not be overly sensitive to the specific direction of the image. Finally, a preprocessed image dataset is generated, which provides standardized and diversified input data for the subsequent model training.

[0085] In substep S102, the model architecture is constructed by designing the hierarchical structure of the convolutional neural network. Under the PyTorch framework, the constructed network contains multiple convolutional layers and pooling layers, which appear alternately to extract image features and reduce feature dimensions. The convolutional layer uses different filters to automatically capture local features of the image, such as edges and textures, while the pooling layer is responsible for reducing the number of parameters and the amount of calculation to prevent overfitting. The ReLU activation function is used after the convolutional layer to introduce nonlinearity to the model, allowing the network to learn more complex feature representations. The BatchNormalization layer is added after each convolutional layer to accelerate the training process and improve the stability of the model. Finally, a model architecture with powerful feature extraction capabilities is constructed, laying a solid foundation for subsequent deep learning tasks.

[0086] In sub-step S103, the model is trained by setting the optimizer and loss function. The Adam optimizer is used, and its learning rate is set to 0.001. The learning rate of each parameter can be adjusted according to the update history of the parameter, thereby improving the efficiency and effect of training. Using CrossEntropyLoss as the loss function can quantify the difference between the model output and the true label, and guide the network to adjust the parameters to reduce this difference. In the PyTorch framework, the torch.optim and torch.nn libraries are used to set and call components, and the weights and biases of the network are optimized through continuous forward propagation and back propagation during iterative training. Finally, a basic trained model is generated, which has the ability to recognize and classify images in the ImageNet dataset, providing a basis for subsequent fine-tuning and application of specific tasks.

[0087] Suppose that in a medical research center, researchers are working to improve early diagnosis technology for neuro-tumors. In the scenario, researchers collected MRI scan image datasets from 100 patients, each of whom provided images containing normal and abnormal (neuro-tumor) areas, totaling 2,000 images. Each image is 256x256 pixels in size, with pixel values ​​ranging from 0 to 255, and the images are labeled as "normal" or "containing tumors." In the study of neuro-tumor image analysis and recognition, researchers used image data to train convolutional neural network models to automatically identify and classify tumors in images. The image dataset was randomly divided into a training set (1,600 images) and a test set (400 images). Each image was preprocessed, including steps such as resizing, normalization, and random horizontal flipping. During the model training phase, the learning rate was set to 0.001, the batch size was 32, and the training rounds were 100. The performance of the model was evaluated by the accuracy, recall, and F1 score on the test set, with the goal of achieving an accuracy of more than 90% to achieve efficient and accurate neuro-tumor recognition.

[0088] See also Figure 3 ,Based on the model after basic training, a model-independent meta-learning algorithm is used to perform refinement and adjustment, so that the model matches the scarce sample neural tumor image data and optimizes the model performance.,The specific steps for generating the adjusted model are as follows;

[0089] S201: Based on the basic trained model, neuro-tumor image data is selected, the image size is resized to 224x224 pixels using Python's PIL library, and the pixel values ​​are normalized to the range of 0 to 1 using the numpy library to generate the selected data set;

[0090] S202: Based on the selected data set, a model-independent meta-learning algorithm is adopted, and the PyTorch framework in Python is used to set the learning rate of the inner loop update to 0.01, and the outer loop learning rate is adjusted to 0.001. Through two-stage training, the parameters of each batch task are fine-tuned in the inner loop, and the global parameters are adjusted in the outer loop to generate a meta-learning matching model;

[0091] S203: Based on the meta-learning matching model, the model is refined and adjusted, and the Adam optimizer in torch.optim is used to optimize the parameters. At the same time, a cross-validation process is set up to evaluate the performance of the model on various neuro-tumor imaging tasks. The number of training rounds and learning rate are adjusted according to performance feedback to generate the adjusted model.

[0092] In sub-step S201, a specific dataset suitable for model training is created through image preprocessing. The selected neuro-tumor image data is processed by Python's PIL library and the image size is adjusted to 224x224 pixels to meet the standard requirements of convolutional neural network input. The pixel values ​​of the image are normalized to the range of 0 to 1 using the numpy library, which helps to improve the stability and convergence speed of model training. Ensuring that all image data are in the same scale and format provides a unified and standardized input for the meta-learning algorithm, thereby improving the efficiency and effectiveness of the subsequent learning process.

[0093] In sub-step S202, the basic trained model is fine-tuned through a model-independent meta-learning algorithm to adapt to the neural tumor image data. In the PyTorch framework, the inner loop update learning rate of meta-learning is set to 0.01 and the outer loop learning rate is set to 0.001. In the two-stage training process, the inner loop is responsible for fine-tuning the parameters of each batch task so that the model can quickly adapt to new tasks; while the outer loop is responsible for adjusting global parameters to improve the generalization ability of the model on various tasks. When faced with scarce neural tumor image data, the model can achieve fast and effective learning through a small number of samples, thereby generating a meta-learning matching model that can better identify and classify different types of neural tumors.

[0094] In sub-step S203, the performance of the meta-learning matching model is enhanced through refinement and adjustment. The model parameters are optimized using the Adam optimizer in PyTorch to ensure that the model achieves optimal performance on the new neural tumor image dataset. A cross-validation process is set up to evaluate the generalization ability of the model on different neural tumor image tasks. According to the results of cross-validation, the number of training rounds and learning rate are appropriately adjusted to avoid overfitting and further improve the diagnostic accuracy of the model. The final adjusted model has stronger generalization ability and higher recognition accuracy, which can effectively identify and classify scarce neural tumor image data, providing more accurate support for clinical diagnosis.

[0095] Suppose that in a medical laboratory focusing on brain disease research, researchers are working with a dataset of 500 neuro-tumor MRI images of different patients. In the dataset, the original size of each image varies, but they have been adjusted to 224x224 pixels through the PIL library and normalized to a pixel value range of 0 to 1. The image labels are divided into "benign tumor" and "malignant tumor" for subsequent model training and testing. In the adjustment based on the model-independent meta-learning algorithm, the researchers set the inner loop learning rate to 0.01 and the outer loop learning rate to 0.001 to adapt to scarce samples. After the meta-learning algorithm adjustment, the accuracy of the model on an independent validation dataset (containing 100 images) increased from 80% to 92%, and the recall rate increased from 78% to 90%. In addition, through cross-validation, the researchers found that the performance of the model was significantly improved after adjusting the learning rate and increasing the number of training rounds (from 50 to 100 rounds). Ultimately, the adjusted model showed higher accuracy and generalization ability in identifying neural tumor images, providing strong technical support for subsequent clinical applications.

[0096] See also Figure 4 ,Based on the adjusted model, through the conditional generative adversarial network, ,the specified tumor type conditions are input, ,to obtain rare or new neural tumor image data, ,and the specific steps of generating the simulated extended image ,dataset are as follows;

[0097] S301: Based on the adjusted model, use Python's PyTorch framework to configure a deep convolutional network structure for the generator. The input layer receives the tumor type label and random noise as conditional inputs. The discriminator is set to a convolutional network to judge the authenticity of the input image and generate the basic cGAN architecture design results.

[0098] S302: Based on the basic cGAN architecture design results, network training was implemented, using the Adam optimizer, with the learning rate set to 0.0002, the batch size set to 64, and the cross entropy loss function. The generator and the discriminator were trained alternately. Through the training loop in the PyTorch framework, the conditional input was controlled so that the generator could generate matching images according to the specified tumor type, and the discriminator was optimized to identify the generated images and the real images, thus generating a trained cGAN model.

[0099] S303: Based on the trained cGAN model, set the conditional label of the specified tumor type, and use the internal conditional embedding and generator network of the model to generate new neural tumor images, including adjusting the generator input conditions, generating images according to the expected tumor type conditions, and generating a simulated extended image dataset.

[0100] In sub-step S301, the generation of specific tumor type images is achieved by constructing the infrastructure of the conditional generative adversarial network (cGAN). Using Python's PyTorch framework, the deep convolutional network structure of the generator is set, in which the input layer receives conditional input consisting of tumor type labels and random noise. This enables the generator to generate corresponding images according to different tumor types. The discriminator is also configured as a convolutional network, whose task is to determine whether the input image is a real image or generated by the generator. It ensures that the generated image can have similar features to the real tumor image, and can generate diversified image samples according to different tumor type conditions.

[0101] In sub-step S302, the image generation capability is optimized by implementing the training process of the cGAN network. In the PyTorch framework, the Adam optimizer is used, the learning rate is set to 0.0002, and the batch size is 64. In addition, the cross entropy is selected as the loss function, and the generator and the discriminator are trained alternately. During the training process, the generator tries to produce realistic images to deceive the discriminator, while the discriminator tries to distinguish between real images and generated images. In this way, the generator and the discriminator continue to improve in mutual competition, and finally the generator is able to generate matching images according to the specified tumor type conditions. The process improves the authenticity of the generated images to better serve medical image analysis and auxiliary diagnosis.

[0102] In sub-step S303, a new neural tumor image dataset is generated by using the trained cGAN model. Specific tumor type condition labels are set, and the generator input conditions are adjusted so that images can be generated according to the expected tumor type conditions. This allows researchers to obtain various rare or specific types of neural tumor images as needed, greatly expanding the existing image dataset. In this way, a simulated extended image dataset containing diverse tumor features and progression stages can be generated, which plays an important role in improving the diagnostic model of neural tumors and enhancing their generalization capabilities.

[0103] Suppose that in a medical imaging institute, the challenge faced by researchers is the lack of image data of rare types of brain nerve tumors, which limits the training and testing of diagnostic models. To this end, it is decided to use conditional generative adversarial networks to enhance the dataset. The research center has an initial dataset containing 300 annotated MRI images, including 100 images annotated as meningioma, glioma, and schwannoma. The original resolution of each image is 512x512 pixels. The researchers used the adjusted model to establish cGAN under the PyTorch framework, with the learning rates of the generator and discriminator set to 0.0002 and 0.0001, respectively, and the batch size set to 64. Through training, an additional 900 images were generated, 300 for each tumor type, and the resolution of each image was also adjusted to 224x224 to meet the training requirements. The images are normalized in pixel values, ranging from 0 to 1, to ensure consistency with the original dataset. The newly generated images were then added to the original dataset, forming a larger and more diverse library of neural tumor images for further training and validation of neural tumor image analysis and recognition models. Through this approach, the researchers were able to significantly improve the performance and generalization of the model, providing a richer and more diverse image resource for the diagnosis of rare neural tumors.

[0104] See also Figure 5 ,Based on the simulated extended image dataset, the feature matching algorithm is used to ,perform image retrieval, and capture images matching with the query image from the dataset. ,The specific steps of generating the matching image retrieval result set are as follows;

[0105] S401: Based on the simulated extended image dataset, the pre-trained VGG16 network is used to directly extract image features through its convolutional layer. The pre-trained model is loaded through tf.keras.applications.VGG16 of the TensorFlow framework, and the image dataset is processed using the predict method. Each image is converted into a feature vector of fixed length, key visual information is captured, and an image feature vector set is generated.

[0106] S402: Based on the image feature vector set, the cosine similarity algorithm is applied to perform feature matching. In the Python environment, the cosine value between two feature vectors is calculated by numpy.dot and numpy.linalg.norm, and the similarity is determined. The feature vectors of each pair of images in the data set are iteratively compared to calculate their similarity and generate a similarity score list.

[0107] S403: Based on the similarity score list, perform image retrieval, filter and query according to the similarity score, use the sorting function of the Python list, sort all images in descending order based on the similarity score through the sorted function, select the image with the highest similarity, and generate a matching image retrieval result set.

[0108] In sub-step S401, the features of each image in the simulated extended image dataset are extracted by using a pre-trained VGG16 network. The pre-trained VGG16 model is loaded using the tf.keras.applications.VGG16 module of the TensorFlow framework. The model has been trained on the large-scale ImageNet dataset and can capture rich image features. By calling the predict method, each image is processed and converted into a fixed-length feature vector that contains the key visual information of the image. This ensures that a set of simplified but information-rich features are extracted from the complex original image, providing a basis for subsequent image retrieval tasks.

[0109] In sub-step S402, the feature vectors are matched by applying the cosine similarity algorithm to find similar images. In the Python environment, the numpy.dot and numpy.linalg.norm functions in the numpy library are used to calculate the cosine value between the two feature vectors, which represents the similarity between the two images in terms of content. By iteratively comparing the feature vectors of each pair of images in the data set and calculating the similarity score between them, a list containing the similarity scores of all image pairs is generated. This allows images to be identified and retrieved based on the similarity of image features rather than relying solely on raw pixel values.

[0110] In sub-step S403, the image retrieval task is performed by using the similarity score list to find the image most similar to the query image. Through the sorting function of Python, the sorted function is used to sort all images in descending order according to the similarity score to ensure that the most similar images can be selected first. According to the set threshold or requirement, a batch of images with the highest similarity are selected as the retrieval results to generate a matching image retrieval result set. It can not only be used to intuitively compare and analyze the similarity of image content, but also provide important reference information in medical image analysis, disease diagnosis and treatment plan formulation.

[0111] Suppose that at a neuroscience research center, the research team is conducting a study aimed at identifying and classifying different types of brain tumors through image analysis methods. It has a dataset of 1,200 MRI brain images, including 400 benign tumor images, 400 malignant tumor images, and 400 normal brain images without tumors. The original resolution of each image is 512x512 pixels. The team uses the VGG16 network pre-trained model to extract image features and convert each image into a feature vector of length 4096. Subsequently, the cosine similarity algorithm is used to compare the feature vectors to identify the image pairs with the highest similarity in the dataset. For example, the feature vector of a query image is [0.2, 0.3, ..., 0.5], and the cosine similarity with the feature vector of a benign tumor image in the dataset [0.21, 0.29, ..., 0.51] reaches 0.98, indicating that the two images are very similar in visual content. As a result, the research team was able to create a matching image retrieval result set that includes the tumor images that are most similar to each query image, which can be used to support the diagnosis and classification research of brain tumors. This helps doctors refer to similar cases during the diagnosis process, thereby improving the accuracy and efficiency of diagnosis.

[0112] See also Figure 6 ,Based on the matching image retrieval result set, a deep learning-based medical image registration method is used to automatically align the images,track the development and changes of the tumor, and compare the time points.,The specific steps to generate the registered image sequence are as follows;

[0113] S501: Based on the matching image retrieval result set, a spatial transformer network is deployed as an image registration method, using the PyTorch framework. The configuration includes a localization network for learning the parameters of the image-to-image transformation. The localization network consists of a convolutional layer and a fully connected layer, and outputs the spatial transformation parameters from the target image to the source image, including rotation, translation, and scaling, to generate image alignment parameter configuration;

[0114] S502: Based on the image alignment parameter configuration, perform the image alignment operation, use the spatial transformation parameters output by the spatial transformer network, obtain the transformed grid through torch.nn.functional.affine_grid, and use

[0115] torch.nn.functional.grid_sample applies the grid to the source image, automatically aligns the image, and generates an aligned image set;

[0116] S503: Based on the aligned image set, the differential image analysis method is used to compare the images in the time series. By calculating the pixel-level differences between images at consecutive time points, the cv2.absdiff function of OpenCV is used to calculate the image differences, revealing the size and morphological changes of the tumor at multiple time points, and generating a registered image sequence.

[0117] In sub-step S501, automatic alignment between images is achieved by deploying a spatial transformer network. Under the PyTorch framework, a localization network is constructed, which consists of convolutional layers and fully connected layers, with the goal of learning the parameters of the spatial transformation from the source image to the target image, including rotation, translation, and scaling. The parameters directly affect the alignment quality of the image. The localization network automatically learns how to adjust the images to align the corresponding parts between them by analyzing the image pairs in the matching image retrieval result set. The generated image alignment parameter configuration is a quantitative description of the required transformation for each image pair, enabling the subsequent steps to perform accurate image alignment.

[0118] In sub-step S502, the automatic alignment process of the images is completed by performing an image alignment operation. Using the spatial transformation parameters output by the spatial transformer network, torch.nn.functional.affine_grid is used to generate the corresponding transformation grid, and torch.nn.functional.grid_sample is used to apply the transformation grid to the source image. The source image is rotated, translated, and scaled to achieve accurate alignment with the target image. The generated aligned image set contains all the adjusted images, and the images are now spatially consistent with the reference image, providing an accurate basis for further analysis.

[0119] In substep S503, the development and changes of the tumor are revealed by using differential image analysis methods. The aligned image set allows direct comparison of images of the same patient at different time points. The pixel-level differences between images at consecutive time points are calculated using OpenCV's cv2.absdiff function, and the differences can reveal the growth or changes of the tumor over time. The resulting registered image sequence provides doctors with an intuitive view of tumor development, allowing the size and morphological changes of the tumor to be tracked, providing key information for diagnosis and treatment planning.

[0120] Suppose that in the radiology department of a hospital, doctors are dealing with a challenging case involving a rare brain nerve tumor. The patient provided a time series of MRI images, containing a total of 50 scans, each with a resolution of 256x256 pixels. The images were annotated as containing a specific type of nerve tumor, but the exact location and size of the tumor changed over time. To better understand the tumor's development pattern, the doctors used a deep learning-based image registration method to analyze the time series. Using a spatial transformer network, each image was automatically adjusted to a common reference frame. The parameters adjusted included rotation (up to ±15 degrees), translation (up to ±10 pixels), and scaling (0.8 to 1.2 times). At this point, all MRI images were successfully aligned so that the tumor area was in a similar position in all images. Using a differential image analysis method, doctors were able to observe the change in size of the tumor from the first image to the last, with the most significant change showing an increase in pixel values, indicating areas of tumor growth. As a result, doctors were able to track the development of the tumor more accurately and provide more targeted treatment recommendations to patients. Not only does it improve the accuracy of diagnosis, it also provides important visual evidence for future treatment planning.

[0121] See also Figure 7 ,Based on the registered image sequence, the synchronization theory analysis method is applied to quantify the synchronization degree between various regions in the image and reveal the cooperative behavior of tumor cells.,The specific steps of generating the synchronization analysis results of the feature image are as follows;

[0122] S601: Based on the registered image sequence, perform time series analysis to extract the frequency characteristics of each region in the image that change with time, use fast Fourier transform, call numpy.fft through Python's NumPy library, and convert the time series of each pixel point with the fft function to generate a frequency spectrum for each region and generate a time-frequency feature set;

[0123] S602: Based on the time-frequency feature set, the phase locking value algorithm is applied to evaluate the synchronization degree between multiple regions in the image. By calculating the consistency of the phase difference in the time-frequency spectrum of multiple regions, the Hilbert transform is performed using the scipy.signal.hilbert function to obtain the instantaneous phase of the signal. It can quantify the phase synchronization between each region in the image sequence, reveal the synchronization degree between regions, and generate regional synchronization analysis results;

[0124] S603: Based on the results of regional synchronization analysis, a synchronization network diagram of the coordinated behavior of tumor cells is drawn. The network diagram is visualized using Python's Matplotlib library to show the intensity of synchronization between various regions in the image sequence, where the network nodes represent the regions in the image, and the intensity of the edges represents the size of the synchronization between regions, revealing the coordinated change pattern between tumor cells and generating synchronization analysis results for characteristic images.

[0125] In sub-step S601, the frequency characteristics of each area in the image that change with time are extracted through time series analysis and processed by fast Fourier transform. In the Python environment, the intensity changes of each pixel in the registered image sequence are converted into frequency space using the numpy.fft.fft function of the NumPy library, and the corresponding frequency spectrum is generated for each image area. Thus, the image is converted from the time domain to the frequency domain, so that the periodicity and frequency characteristics of each area in the image sequence that change with time can be analyzed, providing a data basis for revealing the growth pattern and activity characteristics of tumor cells.

[0126] In substep S602, the degree of synchronization between different regions in the image is evaluated by applying the phase locking value algorithm. The time-frequency feature set is Hilbert transformed using Python's scipy.signal.hilbert function to obtain the instantaneous phase of the signal. This allows the phase differences between different regions in the image sequence to be calculated and compared, and the synchronization between them to be quantified. The phase locking value algorithm reveals whether different regions change in a coordinated manner, providing a quantitative means for understanding the coordinated behavior between tumor cells, which is of great significance for studying the growth dynamics and treatment response of tumors.

[0127] In sub-step S603, the results of synchronization analysis are visualized by drawing a synchronization network diagram of the coordinated behavior of tumor cells. Using Python's Matplotlib library, a network diagram is drawn based on the results of regional synchronization analysis, in which each region in the image sequence is represented as a node in the network, and the strength of the connection between different nodes represents the magnitude of synchronization between regions. The network diagram intuitively displays the interaction and coordinated change patterns between tumor cells, providing doctors and researchers with intuitive tools to understand the behavior and development trends of tumors, which in turn helps to develop more effective treatment strategies.

[0128] Suppose in a neurosurgery research project, researchers focus on analyzing a series of brain tumor MRI time series images from 30 patients. Each patient provided 10 MRI images at different time points, and the resolution of each image was 256x256 pixels. The images were preprocessed and registered to ensure that the images of the same patient at different time points had the same perspective and scale. The research team used fast Fourier transform to analyze the time series of each pixel point to extract the frequency characteristics of each region over time. For example, the frequency of change of pixel intensity in the tumor area over time was recorded, and a frequency spectrum was generated for each area. Then, the phase locking value algorithm was applied to evaluate the degree of synchronization between multiple regions in the image. Through calculation, the phase difference between the tumor area and the surrounding healthy tissue was found, revealing that the growth pattern of tumor cells was significantly different from that of the surrounding healthy cells. Using the information, the researchers constructed a network graph representing the synchronization between different regions, where the nodes represent different regions in the image and the strength of the edges represents the magnitude of synchronization between regions. Through the synchronization network graph, researchers can intuitively observe and analyze the interaction between tumor cells and surrounding tissues, providing new perspectives and in-depth analysis for understanding the development of tumors.

[0129] See also Figure 8 ,Based on the simulated extended image dataset, the matching image retrieval result set, the registered image sequence and the synchronous analysis results of the feature image, a distributed computing framework is used to perform parallel processing and analysis, and data fusion is performed to generate the data distributed processing results. The specific steps are as follows;

[0130] S701: Based on the simulated extended image dataset, the matching image retrieval result set, and the registered image sequence, the Apache Spark environment is configured for data loading, the image data is loaded through the spark.read.format(image) method of Spark, the image dataset is initially read in parallel and format converted, and a uniformly loaded dataset is generated;

[0131] S702: Based on the synchronous analysis results of the uniformly loaded data sets and feature images, the distributed computing capability of Spark is used to apply the specified processing flow to each data set, including using the algorithm in Spark MLlib to extract features from the simulated extended image data set, applying the image alignment algorithm to the matching image retrieval result set, and fusing the registered image sequence with the synchronous analysis results of the feature images, executing tasks in parallel through customized Spark transformation operations and actions, and generating intermediate results of the fusion processing;

[0132] S703: Based on the intermediate results of the fusion processing, a distributed computing framework is used to perform data fusion and analysis. The processing results of all data sets are comprehensively referred to, and Spark's reduceByKey aggregation operation is used to summarize the data. Aggregation analysis is performed on multiple types of image data sets to generate data distributed processing results.

[0133] In sub-step S701, data is loaded by configuring the Apache Spark environment, and the image data is loaded using Spark's spark.read.format(image) method. Image files can be read and format converted in parallel, and the image dataset is loaded as a Spark DataFrame, where each row represents the data of an image file, including the metadata and content of the image. This allows large-scale image datasets to be processed efficiently, providing a unified data format foundation for subsequent image analysis and processing. The generated uniformly loaded dataset exists in the form of a DataFrame, which facilitates subsequent distributed computing and ensures efficient and scalable data processing.

[0134] In sub-step S702, the specified processing flow is applied to each data set through the distributed computing capability of Spark. The algorithm in Spark MLlib is used to extract features from the simulated extended image data set, including converting the image data into a format more suitable for machine learning model processing, such as extracting the color histogram, texture features or edge features of the image. The image alignment algorithm is applied to the matching image retrieval result set, such as using feature point matching and geometric transformation methods to align the images in space to ensure that the same structures between different images can correspond. The registered image sequence is then fused with the synchronous analysis results of the feature image, and tasks are executed in parallel through customized Spark conversion operations and actions. The distributed processing capability of Spark ensures the rapid processing of large-scale data sets, and the generated intermediate results of the fusion processing contain rich image feature information, which provides the necessary input for image recognition and analysis.

[0135] In sub-step S703, the processing results of all data sets are integrated by using a distributed computing framework for data fusion and analysis. Spark's reduceByKey aggregation operation is used to perform aggregation analysis on multiple types of image data sets, and the values ​​in the data sets are aggregated by the specified key values. For image data processing, image features, types, or other key indicators can be grouped and summarized. Image analysis results from different sources and stages can be processed and analyzed uniformly, and the generated data distributed processing results provide a comprehensive data view for subsequent recognition and analysis tasks, optimizing the data integration quality and processing efficiency. It not only improves the accuracy of data processing, but also provides strong data support for in-depth analysis of neuro-tumor images, making the final image recognition and analysis work more accurate and efficient.

[0136] Suppose that in a neural tumor image analysis project, researchers have collected a dataset of 1,000 high-resolution brain MRI images. The images are divided into two categories: 500 images representing neural tumors (marked as 1) and 500 healthy brain images (marked as 0). The data items for each image include the image ID, the original pixel value matrix of the image, the image resolution (such as 256x256 pixels), and the image label (with tumor / without tumor). In the simulated extended image dataset, 200 simulated neural tumor images were added through the conditional generative adversarial network (cGAN), and the simulated values ​​of the images imitated the various shapes and sizes of real tumors and the diversity of the background at the pixel level. Each simulated image includes the image ID, the generated pixel value matrix, the image resolution, and the type label of the simulated tumor (such as glioma, meningioma, etc.). Through the data items and simulated values, researchers can train and verify the model to further improve the accuracy and efficiency of the neural tumor image analysis and recognition method.

[0137] See also Fig. 9 ,Based on the data distributed processing results and the synchronous analysis results of the feature images, the machine learning algorithm is used to perform neural tumor image recognition and analysis again to optimize the recognition accuracy and efficiency. The specific steps for generating image recognition optimization results are as follows;

[0138] S801: Based on the results of data distributed processing and synchronous analysis of feature images, prepare a data set for machine learning training, integrate data features, and use feature selection technology to screen neural tumor image recognition features based on contribution, including selecting K features through the SelectKBest method in Python's Scikit-learn library, optimizing the subsequent machine learning model training process, and generating an optimized training data set;

[0139] S802: Based on the optimized training data set, a convolutional neural network model in deep learning is used for training. The model configuration includes setting the network hierarchy, activation function and optimizer, including using the Keras library to build the model, setting the Adam optimizer, and selecting categorical_crossentropy as the loss function. The model training process is executed so that it can learn the patterns and features in the data and generate a trained CNN model.

[0140] S803: Based on the trained CNN model, identify and analyze neural tumor images, use the model to predict the test data set, evaluate the recognition accuracy and efficiency of the model, calculate the accuracy, recall and F1 score performance indicators of the model on the test set, use the classification_report method in the Scikit-learn library to generate model performance information, and generate image recognition optimization results.

[0141] In sub-step S801, a data set for machine learning training is prepared by integrating the results of distributed data processing and the results of synchronous analysis of feature images. The data format includes the feature vector and corresponding label of the image. The feature vector is represented by the numerical value obtained after the image is preprocessed and feature extraction steps, and the label is whether the image contains classification information of neural tumors. The SelectKBest method in Python's Scikit-learn library is used to select the K most contributing features according to the F value. This process evaluates the correlation between each feature and the target variable based on statistical tests, thereby optimizing the input feature set of the subsequent machine learning model training process. The optimized training data set not only reduces the feature dimension and improves the training efficiency, but also helps to improve the generalization ability and recognition accuracy of the model.

[0142] In sub-step S802, the optimized training data set is trained by using a convolutional neural network model (CNN) built using the Keras library. The network hierarchy of the model includes several convolutional layers (Conv2D) for extracting image features, pooling layers (MaxPooling2D) for reducing feature dimensions and preventing overfitting, and at least one fully connected layer (Dense) for classification. The model uses ReLU as the activation function, Adam optimizer for parameter optimization, and categorical_crossentropy as the loss function to handle multi-classification problems. During the model training process, the training data is fed to the model through the fit method. After multiple iterations of training, the model can learn the patterns and features in the data and generate a trained CNN model. Efficient learning of specific neuro-tumor image recognition patterns is achieved, improving the accuracy and efficiency of recognition.

[0143] In sub-step S803, the recognition accuracy and efficiency of the model are evaluated by predicting the test data set on the trained CNN model. The classification_report method in the Scikit-learn library is used to calculate and report the performance indicators such as accuracy, recall, and F1 score of the model on the test set. The indicators provide a comprehensive evaluation of the model performance. The accuracy index reflects the ability of the model to correctly identify neural tumor images, the recall rate shows the proportion of tumor images recognized by the model to all actual tumor images, and the F1 score is the harmonic average of the accuracy and recall rate, which comprehensively reflects the performance of the model. The generated image recognition optimization results prove the effectiveness and reliability of the model in practical applications, providing a basis for subsequent clinical applications.

[0144] Suppose that in a research project focusing on the early diagnosis of brain tumors, the research team prepared a dataset of 1,500 MRI images for training and testing a deep learning-based neural tumor image analysis and recognition model. The images are subdivided into three categories: 500 images show the presence of glioma, 500 images show meningioma, and the remaining 500 images are healthy brain images without tumors. The data items for each image include the image ID (such as TUMOR_001), the actual category label of the image (glioma, meningioma, no tumor), and the preprocessed pixel value matrix, where the image resolution is uniformly adjusted to 224x224 pixels. Through the conditional generative adversarial network (cGAN), the research team generated an additional 300 simulated tumor images to enhance the diversity of the dataset. The simulated values ​​of the images are designed to simulate the characteristics of tumors of different sizes and shapes. When performing feature selection, key features such as image texture, edge strength, and regional shape were selected. The feature vector of each image consists of 256 values ​​for training the deep learning model. Therefore, the research team aims to improve the generalization ability and recognition accuracy of the model to provide a more precise analysis tool for the early diagnosis of neuro-tumors.

[0145] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for analyzing and identifying neural tumor images, characterized in that: The following steps are involved: Based on the ImageNet image dataset, a convolutional neural network is used to train the learning model, obtain feature recognition capabilities, and perform feature extraction to generate a basic trained model; Based on the basic trained model, a model-independent meta-learning algorithm is used to perform refinement and adjustment, so that the model matches the scarce sample neural tumor image data, and the model performance is optimized to generate an adjusted model; Based on the adjusted model, using a conditional generative adversarial network, inputting specified tumor type conditions, acquiring rare or new neural tumor image data, and generating a simulated extended image dataset; Based on the simulated extended image data set, a feature matching algorithm is used to perform image retrieval, images matching the query image are captured from the data set, and a matching image retrieval result set is generated; Based on the matching image retrieval result set, a deep learning-based medical image registration method is used to automatically align the images, track the development and changes of the tumor, and compare the time points to generate a registered image sequence; Based on the registered image sequence, applying the synchronization theory analysis method, quantifying the synchronization degree between various regions in the image, revealing the coordinated behavior of tumor cells, and generating synchronization analysis results of characteristic images; Based on the simulated extended image data set, the matching image retrieval result set, the registered image sequence and the synchronous analysis result of the feature image, a distributed computing framework is used to perform parallel processing and analysis, and data fusion is performed to generate data distributed processing results; Based on the distributed data processing results and the synchronous analysis results of the feature images, a machine learning algorithm is used to perform neural tumor image recognition and analysis again to optimize recognition accuracy and efficiency and generate image recognition optimization results.

2. The neural tumor image analysis and identification method according to claim 1, characterized in that: The model after basic training includes a network structure after feature layer extraction capability, classification accuracy and adaptability are optimized; the adjusted model includes network parameters optimized for specified neural tumor image characteristics, a learning strategy matching a data-scarce environment, and a loss function for optimizing diagnostic accuracy; the simulated and extended image data set includes a diverse tumor feature simulation, images of multiple tumor progression stages, and tumor images under variable backgrounds; the matched image retrieval result set includes a list of images matched according to image content similarity, image features selected based on similarity scores, and index tags for retrieval; the registered image sequence includes tumor change images arranged in time series and automatically aligned, alignment parameters for analyzing tumor progression, and dynamic features for change tracking; the synchronous analysis results of the feature images include a phase consistency index that reveals the coordinated behavior of tumor cells, a dynamic change map of tumor growth synchronization, and quantitative analysis information of regional interactions; the data distributed processing results include data aggregation indicators obtained through parallel computing, optimized computing resource allocation recommendations, and data processing performance evaluation information; the image recognition optimization results include a recognition accuracy index after reanalysis, reduced misjudgment rate statistics, and optimized diagnostic efficiency for specified tumor types.

3. The neural tumor image analysis and identification method according to claim 1, characterized in that: Based on the ImageNet image dataset, a convolutional neural network is used to train the learning model, obtain feature recognition capabilities, and perform feature extraction. The specific steps to generate the basic trained model are as follows; Based on the ImageNet image dataset, the image size is adjusted to 224x224 pixels using the Resize method in the torchvision.transforms library, and the Normalize method scales the image pixel values ​​to between 0 and 1. The RandomHorizontalFlip method performs random horizontal flipping on the image to generate a preprocessed image dataset; Based on the preprocessed image dataset, a hierarchical structure design of a convolutional neural network is performed, the network is constructed using the PyTorch framework, multiple Conv2D layers and MaxPooling2D layers are added to alternately extract features, ReLU is used as an activation function for nonlinear processing, and the BatchNormalization layer is used to standardize the input of each layer of the network to generate a model architecture design result; Based on the model architecture design results, model training was performed using the Adam optimizer, with the learning rate set to 0.

001. The loss function used CrossEntropyLoss to handle multi-classification problems. The associated functions in the torch.optim and torch.nn libraries were used for configuration. The network parameters were iteratively optimized to generate a model after basic training.

4. The neural tumor image analysis and identification method according to claim 1, characterized in that: Based on the model after the basic training, a model-independent meta-learning algorithm is used to perform refinement and adjustment to make the model match the neural tumor image data of scarce samples and optimize the model performance. The specific steps of generating the adjusted model are as follows; Based on the basic trained model, neural tumor image data is selected, the image size is adjusted to 224x224 pixels using Python's PIL library, and the pixel values ​​are normalized to a range of 0 to 1 using the numpy library to generate the selected data set; Based on the selected data set, a model-independent meta-learning algorithm is adopted, and the PyTorch framework in Python is used. The learning rate of the inner loop update is set to 0.01, and the learning rate of the outer loop is adjusted to 0.

001. Through two-stage training, the parameters of each batch task are fine-tuned in the inner loop, and the global parameters are adjusted in the outer loop to generate a meta-learning matching model; Based on the meta-learning matching model, the model was refined and adjusted, and the Adam optimizer in torch.optim was used for parameter optimization. A cross-validation process was set up to evaluate the performance of the model on various neuro-tumor imaging tasks. The number of training rounds and learning rate were adjusted according to performance feedback to generate the adjusted model.

5. The neural tumor image analysis and identification method according to claim 1, characterized in that: Based on the adjusted model, the specific steps of generating a simulated extended image data set by conditional generative adversarial network, inputting specified tumor type conditions, obtaining rare or new neural tumor image data, and generating the following are performed; Based on the adjusted model, using Python's PyTorch framework, a deep convolutional network structure is configured for the generator, the input layer receives the tumor type label and random noise as conditional input, and the discriminator is set as a convolutional network to judge the authenticity of the input image, generating a basic cGAN architecture design result; Based on the basic cGAN architecture design results, network training was implemented, using the Adam optimizer, with the learning rate set to 0.0002, the batch size set to 64, and the cross entropy loss function. The generator and the discriminator were alternately trained, and through the training loop in the PyTorch framework, the conditional input was controlled so that the generator could generate matching images according to the specified tumor type, and the discriminator was optimized to identify the generated images and the real images, thereby generating a trained cGAN model; Based on the trained cGAN model, conditional labels of specified tumor types are set, and new neural tumor images are generated by using the conditional embedding and generator network inside the model, including adjusting the generator input conditions, generating images according to the expected tumor type conditions, and generating a simulated extended image dataset.

6. The neural tumor image analysis and identification method according to claim 1, characterized in that: Based on the simulated extended image dataset, a feature matching algorithm is used to perform image retrieval, and images matching the query image are captured from the dataset. The specific steps of generating a matching image retrieval result set are as follows: Based on the simulated extended image dataset, the trained VGG16 network is used to directly extract image features through its convolution layer, the training model is loaded through tf.keras.applications.VGG16 of the TensorFlow framework, and the image dataset is processed using the predict method to convert each image into a feature vector of fixed length, capture key visual information, and generate an image feature vector set; Based on the image feature vector set, the cosine similarity algorithm is applied to perform feature matching. In the Python environment, the cosine value between two feature vectors is calculated by numpy.dot and numpy.linalg.norm, and the similarity is determined. The feature vectors of each pair of images in the data set are iteratively compared to calculate their similarity and generate a similarity score list. Based on the similarity score list, image retrieval is performed, and screening and querying are performed according to the similarity score. The sorting function of the Python list is used to sort all images in descending order based on the similarity score through the sorted function, and the image with the highest similarity is selected to generate a matching image retrieval result set.

7. The neural tumor image analysis and identification method according to claim 1, characterized in that: Based on the matching image retrieval result set, a deep learning-based medical image registration method is used to automatically align images, track the development and changes of tumors, and compare time points. The specific steps of generating a registered image sequence are as follows; Based on the matching image retrieval result set, a spatial transformer network is deployed as an image registration method, using a PyTorch framework, and the configuration includes a localization network for learning parameters of image-to-image transformation. The localization network consists of a convolutional layer and a fully connected layer, and outputs spatial transformation parameters from the target image to the source image, including rotation, translation, and scaling, to generate image alignment parameter configuration; Based on the image alignment parameter configuration, perform an image alignment operation, use the spatial transformation parameters output by the spatial transformer network, obtain a transformed grid through torch.nn.functional.affine_grid, apply the grid to the source image using torch.nn.functional.grid_sample, perform automatic image alignment, and generate an aligned image set; Based on the aligned image set, a differential image analysis method is used to compare images in the time series. By calculating the pixel-level differences between images at consecutive time points, the cv2.absdiff function of OpenCV is used to calculate the image differences, revealing the size and morphological changes of the tumor at multiple time points, and generating a registered image sequence.

8. The neural tumor image analysis and identification method according to claim 1, characterized in that: Based on the registered image sequence, the synchronization theory analysis method is applied to quantify the synchronization degree between various regions in the image and reveal the coordinated behavior of tumor cells. The specific steps of generating the synchronization analysis result of the characteristic image are as follows; Based on the registered image sequence, time series analysis is performed to extract the frequency characteristics of each area in the image that change with time, and fast Fourier transform is used. Through the Python NumPy library, numpy.fft is called, and the fft function is used to transform the time series of each pixel point, generate a frequency spectrum for each area, and generate a time-frequency feature set; Based on the time-frequency feature set, a phase locking value algorithm is applied to evaluate the degree of synchronization between multiple regions in the image. By calculating the consistency of the phase difference in the time-frequency spectrum of multiple regions, the scipy.signal.hilbert function is used to perform Hilbert transform to obtain the instantaneous phase of the signal, which can quantify the phase synchronization between each region in the image sequence, reveal the degree of synchronization between regions, and generate regional synchronization analysis results; Based on the regional synchronization analysis results, a synchronization network diagram of the coordinated behavior of tumor cells is drawn. The network diagram is visualized using Python's Matplotlib library to show the intensity of synchronization between multiple regions in the image sequence, where the network nodes represent the regions in the image, and the intensity of the edges represents the size of the synchronization between the regions, revealing the coordinated change pattern between tumor cells and generating synchronization analysis results of characteristic images.

9. The neural tumor image analysis and identification method according to claim 1, characterized in that: Based on the simulated extended image data set, the matching image retrieval result set, the registered image sequence and the synchronous analysis results of the feature image, a distributed computing framework is used to perform parallel processing and analysis, and data fusion is performed to generate the data distributed processing results in the following specific steps; Based on the simulated extended image dataset, the matching image retrieval result set and the registered image sequence, the Apache Spark environment is configured to load data, the image data is loaded through the spark.read.format(image) method of Spark, the image dataset is initially read in parallel and format converted, and a uniformly loaded dataset is generated; Based on the synchronous analysis results of the uniformly loaded data sets and feature images, the distributed computing capability of Spark is used to apply a specified processing flow to each data set, including using the algorithm in Spark MLlib to perform feature extraction on the simulated extended image data set, applying the image alignment algorithm to the matching image retrieval result set, and fusing the registered image sequence with the synchronous analysis results of the feature images, executing tasks in parallel through customized Spark conversion operations and actions, and generating intermediate results of the fusion processing; Based on the intermediate results of the fusion processing, a distributed computing framework is used to perform data fusion and analysis. The processing results of all data sets are comprehensively referred to, and Spark's reduceByKey aggregation operation is used to summarize the data. Aggregation analysis is performed on multiple types of image data sets to generate data distributed processing results.

10. The neural tumor image analysis and identification method according to claim 1, characterized in that: Based on the data distributed processing results and the synchronous analysis results of the feature images, the specific steps of using a machine learning algorithm to perform neural tumor image recognition and analysis again to optimize recognition accuracy and efficiency and generate image recognition optimization results are as follows; Based on the distributed data processing results and the synchronous analysis results of the feature images, preparing a data set for machine learning training, integrating the features of the data, and screening the neural tumor image recognition features based on the contribution, including selecting K features through the SelectKBest method in the Scikit-learn library of Python, optimizing the subsequent machine learning model training process, and generating an optimized training data set; Based on the optimized training data set, a convolutional neural network model in deep learning is used for training. The configuration of the model includes setting the network hierarchy, activation function and optimizer, including using the Keras library to build the model, setting the Adam optimizer, selecting categorical_crossentropy as the loss function, and executing the model training process so that it can learn the patterns and features in the data and generate a trained CNN model; Based on the trained CNN model, the neural tumor images are recognized and analyzed, the model is used to predict the test data set, the recognition accuracy and efficiency of the model are evaluated, the accuracy, recall rate and F1 score performance indicators of the model on the test set are calculated, and the classification_report method in the Scikit-learn library is used to generate model performance information and generate image recognition optimization results.

Images