Medical image recognition method and system integrating deep learning and big data algorithm

Abstract

The invention discloses a medical image recognition method and system integrating deep learning and a big data algorithm, and aims to improve the recognition efficiency and accuracy of medical images. The method comprises the following steps: collecting medical image data from a plurality of medical institutions, and constructing a large-scale medical image database; carrying out standardization processing on the collected medical image, wherein the standardization processing comprises image enhancement, denoising and size adjustment; based on the processed data, constructing a deep learning model by using a convolutional neural network, and performing feature extraction in combination with a big data algorithm; training the deep learning model by adopting an integrated learning method, and optimizing model parameters; after model training is completed, the input medical image is recognized in real time, and a recognition result is fed back to a doctor; and an identification result and related image data are stored in a cloud end to provide support for subsequent analysis. By means of the method, the medical image recognition efficiency can be effectively improved, and more reliable auxiliary support is provided for clinic.

Description

Technical Field

[0001] The present invention relates to the field of medical image processing, and in particular to a medical image recognition method and system integrating deep learning and big data algorithms. Background Art

[0002] With the rapid development of medical imaging technology, the scale and complexity of medical imaging data have increased significantly. The widespread application of various medical images such as CT, MRI, and X-rays provides an important basis for the early diagnosis and treatment of diseases. However, traditional medical image analysis methods often rely on the experience of professional radiologists and face the following major challenges: Traditional image analysis methods are usually manually designed based on image processing and feature extraction, with slow processing speed and strong dependence on doctors' professional knowledge, which is difficult to meet the clinical needs for real-time diagnosis; Due to the diversity and complexity of image features, manual analysis may lead to a decrease in diagnostic accuracy, especially when processing a large amount of image data, which is prone to fatigue and omissions, affecting the final diagnosis results; There is a lack of effective data sharing and integration mechanisms among medical institutions, resulting in medical imaging data being scattered on different platforms, and it is impossible to fully utilize rich historical data for model training and optimization; Although deep learning has shown excellent performance in the field of image recognition, its training process requires a large amount of high-quality labeled data, and traditional image acquisition and annotation methods are difficult to meet this demand.

[0003] In order to solve the above problems, in recent years, researchers have begun to explore the combination of deep learning and big data technology in order to improve the efficiency and accuracy of medical image analysis. Deep learning models can automatically learn features in images and reduce reliance on manually designed features. At the same time, big data technology can help collect, process and store large-scale image data to provide support for model training.

[0004] Although some medical image recognition methods based on deep learning have been proposed, there are still problems such as insufficient model generalization ability, unbalanced training data and high computing resource requirements. Therefore, a novel medical image recognition method that integrates deep learning and big data algorithms is urgently needed to achieve more efficient and accurate medical image analysis and provide more reliable support for clinical decision-making. Summary of the invention

[0005] The present invention provides a medical image recognition method integrating deep learning and big data algorithms, which includes the following steps:

[0006] Collect medical imaging data from multiple medical institutions and build a large-scale medical imaging database;

[0007] Standardized processing of collected medical images, including image enhancement, denoising, and resizing;

[0008] Based on the processed data, a deep learning model is constructed using a convolutional neural network and feature extraction is performed in combination with a big data algorithm;

[0009] Use ensemble learning methods to train deep learning models and optimize model parameters;

[0010] After the model training is completed, the input medical images are recognized in real time and the recognition results are fed back to the doctor;

[0011] The recognition results and their related image data are stored in the cloud to provide support for subsequent analysis.

[0012] As described above, a medical image recognition method integrating deep learning and big data algorithms, wherein the deep learning model adopts a convolutional neural network structure, and the model can be adaptively adjusted according to different types of medical images.

[0013] As described above, a medical image recognition method integrating deep learning and big data algorithms, wherein the data preprocessing step includes image enhancement, denoising and standardization processing, etc., to ensure that the model can handle diverse data inputs during training.

[0014] As described above, a medical image recognition method integrating deep learning and big data algorithms is described, wherein, during the model training process, the deep learning model is trained by adopting an integrated learning method to optimize the model parameters.

[0015] As described above, a medical image recognition method integrating deep learning and big data algorithms, wherein the real-time recognition also includes providing the confidence value of the recognition result to the doctor to assist in clinical judgment.

[0016] As described above, a medical image recognition method integrating deep learning and big data algorithms is described, wherein the system is able to generate recognition reports for different disease categories and analysis charts of model performance to help doctors understand the recognition capabilities of the model and its effects in practical applications.

[0017] As described above, a medical image recognition method integrating deep learning and big data algorithms is described, wherein the cloud storage has high security and data encryption mechanism to protect the privacy of medical image data, and supports regular data backup and recovery functions to ensure the security and integrity of the data.

[0018] The present invention also provides a medical image recognition system integrating deep learning and big data algorithms, wherein the system comprises:

[0019] A data collection module for collecting medical imaging data from multiple medical institutions;

[0020] Data preprocessing module, used to standardize image data;

[0021] Model training module, used to build and train deep learning models;

[0022] A real-time recognition module is used to perform real-time recognition of input medical images;

[0023] The data storage module is used to store the recognition results and image data in the cloud.

[0024] The present invention also provides a computer storage medium, comprising: at least one memory and at least one processor;

[0025] The memory is used to store one or more program instructions;

[0026] A processor is used to run one or more program instructions to execute a medical image recognition method that integrates deep learning and big data algorithms.

[0027] The beneficial effects achieved by the present invention are as follows: The present invention adopts a deep learning algorithm to automatically extract key features in medical images, thereby significantly improving the accuracy of disease identification. It integrates deep learning and big data technology to achieve rapid processing and real-time identification of medical images, shortening the time doctors spend analyzing images. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0029] Figure 1 This is a flow chart of a medical image recognition method integrating deep learning and big data algorithms provided in Example 1 of the present application;

[0030] Figure 2 This is a schematic diagram of a medical image recognition system that integrates deep learning and big data algorithms provided in Example 2 of the present application. DETAILED DESCRIPTION

[0031] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0032] Embodiment 1

[0033] like Figure 1 As shown, the first embodiment of the present application provides a medical image recognition method integrating deep learning and big data algorithms, comprising the following steps:

[0034] Step S10: Collect medical imaging data from multiple medical institutions to build a large-scale medical imaging database;

[0035] Data collection is the first step of the medical image recognition method, which aims to systematically collect medical image data from multiple medical institutions to build a large-scale medical image database. The specific implementation steps are as follows:

[0036] Select cooperative medical institutions: Establish cooperative relationships with multiple medical institutions with rich imaging resources, including large general hospitals, specialized hospitals and research institutions. Ensure that these institutions have a good reputation and technical support capabilities in the field of medical imaging. When selecting cooperative institutions, consider the diversity of imaging types (such as CT, MRI, X-ray, etc.) and disease types (such as cardiovascular diseases, tumors, orthopedic diseases, etc.) to cover different clinical scenarios and patient groups.

[0037] Development of data collection protocol: Develop a detailed data collection protocol to clarify the scope, format and requirements of data collection, and ensure that the collected data complies with relevant laws, regulations and ethical standards. All data should be in a unified medical imaging format, such as DICOM (Digital Imaging and Communications in Medicine) format, for subsequent processing.

[0038] Data collection process: By connecting with the hospital's information system, the medical images of patients are automatically collected. Real-time data collection is achieved to ensure the timeliness of data updates. The collection of each image data needs to record relevant metadata, including patient information (such as age, gender, medical history), image type, collection date, equipment model, etc. These metadata are crucial for subsequent data analysis and model training. To ensure data quality, the following formula is used to evaluate the diversity and integrity of the collected image data: Among them, Q represents the data quality index; M is the number of medical institutions from which the data comes; N j is the number of images provided by the jth institution; N total is the total number of images provided by all institutions; Var(C j ) is the variance of the disease category distribution of the jth institution’s images. By calculating the data quality index, the balance and representativeness of the data from each institution are ensured.

[0039] Data labeling and storage: Through cooperation with radiologists, professional labeling of images, including image features, disease diagnosis and treatment plans, etc. Labeled data will serve as an important basis for training models. The collected images and their labeling information will be stored in the cloud database to ensure data security and accessibility. Efficient storage solutions are used to support fast reading and writing of large-scale data.

[0040] Real-time update mechanism: Establish a real-time data update mechanism to regularly obtain new data from partner hospitals to keep the image database up to date and complete. Each data update requires a quality assessment to ensure that the new data meets the collection standards, and review the existing data to eliminate unqualified data.

[0041] Step S20, performing standardization processing on the collected medical images, including image enhancement, denoising and size adjustment;

[0042] Data preprocessing is a key step to ensure the quality and usability of medical images. This step includes image enhancement, denoising, and normalization, which aims to improve the quality of image data in order to prepare for subsequent deep learning model training.

[0043] In order to improve the visualization effect of the image, a comprehensive image enhancement function E is used to make the details of the image clearer by adjusting the contrast, brightness and saturation. The definition of the enhancement function is as follows: Where I(x,y) represents the pixel value of the original image; α represents the contrast adjustment parameter, which controls the enhancement intensity; μ is the mean value of the image; β is the brightness adjustment parameter; k represents the domain range, which is used to calculate the local average; γ represents the saturation adjustment parameter, which is used to dynamically adjust different parameters and enhance according to the overall characteristics of the image to ensure the visualization of details.

[0044] In order to reduce the noise in the image, a denoising function D based on wavelet transform is used. This function combines local features and global structures and is defined as follows: Among them, W j represents the j-th layer wavelet coefficient, which represents information of different frequencies; (x0, y0) represents the center coordinates of the image; σ represents the parameter that controls the denoising intensity, and a smaller value will lead to a stronger denoising effect; n represents the number of wavelet transform layers. Through this formula, the edge features of the image can be effectively retained while denoising.

[0045] To ensure the uniformity of the image, the image pixel values ​​are normalized to between 0 and 1 using the following normalization formula: Among them, S represents the standardized pixel value; I(x, y) represents the pixel value of the original image; μ local Represents the mean of the local area of ​​the image, which can improve the retention of local features; σ globalRepresents the global standard deviation of the image, which is used to ensure the overall consistency of the data; ∈ is a small constant to prevent division by zero; ∑ i,j I(x+i,y+j) represents the sum of all pixels in the local window centered at pixel (x,y); N local Represents the number of local pixels, used to calculate the local mean.

[0046] Step S30: Based on the processed data, a deep learning model is constructed using a convolutional neural network, and feature extraction is performed in combination with a big data algorithm;

[0047] In this step, a deep learning model is constructed based on a convolutional neural network, and adaptive feature extraction is performed in combination with a big data algorithm. Traditional convolutional neural networks extract image features through fixed convolution kernels and pooling operations, but in practical applications, the diversity and complexity of image features require the model to automatically adapt and optimize the feature extraction process. The present invention proposes an adaptive feature extraction mechanism that combines adaptive convolution kernels, dynamic feature selection, and group learning strategies to improve the performance of the model in medical image recognition. The specific steps are as follows:

[0048] Adaptive convolution kernel adjustment: In traditional convolutional neural networks, the size and shape of the convolution kernel are usually fixed. However, due to the large differences in features of different regions in medical images, fixed convolution kernels often cannot fully capture all important information. Therefore, the present invention proposes an adaptive convolution kernel adjustment mechanism, which enables the convolution kernel to automatically adjust its size and shape according to the local features of the input image. Specifically, the size and shape of the convolution kernel S L It is dynamically generated based on the local statistical characteristics of the image data. For example, image segmentation or clustering analysis can be used to determine the different regions and features of the image. The output F of the convolutional layer L (x,y) is represented as: Among them, N L is the number of convolution kernels used in the Lth convolutional layer; F L (x, y) represents the output feature map of the Lth layer; W L,i (I(x,y),S L ) represents the weight of the i-th convolution kernel in the L-th layer, which is based on the current image I(x, y) and the feature S of the specific layer L L Dynamically generated; S L Represents the shape and size of the convolution kernel, which is adjusted according to the local features of the image; F L-1 (x,y) represents the output feature map of the previous layer. In this way, the convolution kernel can be adaptively adjusted to extract local features for different regions and enhance the ability to capture image details.

[0049] Dynamic feature selection and weight update: In image data, not all features contribute equally to the classification task. Traditional convolutional neural networks process features uniformly after extracting them at each layer. However, in a big data environment, the number and dimensions of features are often very large. Therefore, this paper introduces a dynamic feature selection and weighting mechanism, which selects and updates features in each layer in a weighted manner according to their classification contribution. Each feature F i The weight α i It will be dynamically adjusted according to its contribution to the classification task. Specifically, the final optimized feature can be expressed as: Where M represents the total number of extracted features; F optimized is the final feature representation after weighting; F i represents the i-th extracted feature; α i Represents the weight coefficient of the i-th feature, which is dynamically adjusted according to its importance during the training process. i The update is achieved through the back-propagation algorithm, which optimizes each feature based on its impact on model performance. This mechanism enables the model to automatically learn which features are more important for the classification task, thereby improving the accuracy of the model.

[0050] Group learning feature fusion: In medical image recognition, image data usually has a complex structure, and a single feature extraction strategy may not be able to fully capture all information. Therefore, this step combines the group learning strategy in the big data algorithm, using distributed computing and cluster learning to extract features from multiple sub-networks and fuse these features into a unified feature representation. Assume that the network is divided into multiple sub-networks N1, N2, ..., N k , each sub-network independently extracts features from different subsets of the data. Each sub-network N j The extracted feature is F j , the final fused feature F final It can be expressed as: Among them, F final represents the final fused feature representation; K represents the number of sub-networks; F j represents the features extracted by the jth sub-network; β j represents the weight coefficient of the jth sub-network, indicating the importance of the sub-network in the final feature fusion. These weight coefficients β j It is dynamically updated through group learning to ensure that the final fused features can effectively represent the common features of all image data while reducing possible redundant information. Through adaptive convolution kernels, dynamic feature selection and group learning fusion, this model can continuously optimize the feature extraction process according to the characteristics of image data.

[0051] Step S40: Based on the processed data, a deep learning model is constructed using a convolutional neural network, and feature extraction is performed in combination with a big data algorithm;

[0052] The convolutional neural network is initialized and trained using the training set. During the training process, the gradient descent method is used to optimize the loss function and gradually update it in combination with the adaptive learning rate. Assume that the loss function is The model parameter is θ, and the parameter is optimized by gradient descent. The formula is as follows: Among them, θ t is the model parameter at the current moment; τ t is the learning rate at the current moment, which is dynamically adjusted according to the training progress; is the gradient of the loss function with respect to the parameters. In order to effectively avoid the local minimum problem, stochastic gradient descent is used in this step, which accelerates the training process and avoids falling into the local minimum by randomly extracting part of the data for gradient calculation at each update. In order to accelerate convergence and improve training results, the learning rate τ t It will be adaptively adjusted with the training round t. The following adaptive learning rate adjustment formula is used to ensure that the model is optimized with different learning rates at different stages of training: Among them, τ0 is the initial learning rate; γ is the decay factor, which controls the decay speed of the learning rate; t is the current training round. The function of this formula is to gradually reduce the learning rate as the training progresses, so as to ensure that the model can be adjusted more finely when approaching the optimal solution to avoid over-adjustment. In order to avoid over-training of the model, a dynamic early stopping mechanism is introduced to determine when to stop training by calculating the gain function during the training process. The gain function is defined as: in, and are the loss function values ​​of the current and previous rounds of training, G t It measures the rate of decrease of the loss function during training. If the value of the gain function is less than a preset threshold in N consecutive rounds of training, the training is stopped to prevent overfitting.

[0053] After the model training and optimization are completed, the model performance is further optimized through the integration method, and the results of multiple optimizations are fused through the weighted average method to obtain the final prediction output. The fusion formula is: in, is the final prediction result; K represents the number of sub-models involved in the fusion; is the predicted value of the i-th model; α i The weight of the model is dynamically adjusted based on the training performance of the model; is the average of all predicted values; β is the adjustment factor to control the impact of prediction deviation.

[0054] Step S50: After the model training is completed, the input medical image is recognized in real time, and the recognition result is fed back to the doctor;

[0055] In this step, the input medical images are processed and recognized in real time to generate corresponding diagnosis results, which are then fed back to the doctor in a timely manner through a feedback mechanism. This process ensures that medical decisions can be made in the shortest possible time and provides effective support to improve the efficiency and accuracy of diagnosis and treatment.

[0056] When medical images are input into the system, they are first preprocessed to ensure that the input data meets the requirements of the model. The preprocessed medical images are then input into the trained deep learning model for real-time feature extraction and prediction. Based on the convolutional neural network architecture, the model extracts key features from the image through multiple convolutional layers, pooling layers, and fully connected layers. Once the model completes the inference of the image, the output diagnosis result will be transmitted to the doctor through a real-time feedback mechanism. To enhance the effectiveness of the feedback, the model will not only provide simple classification results, but also come with corresponding confidence scores. Based on this feedback information, doctors can quickly understand the diagnostic results of the image and its credibility, and make further clinical decisions. In order to improve doctors' understanding of the diagnostic results and decision-making efficiency, the system will also present the recognition results in a visual way and use a heat map display method to mark the key areas in the image with the predicted results.

[0057] To further improve the accuracy of diagnosis and support doctors’ decision-making, the system will combine multimodal data (such as medical images, medical records, etc.) to generate a comprehensive feedback report. The comprehensive report includes the following:

[0058] Diagnostic results: The system's analysis results of medical images, including lesion type, location, etc.

[0059] Confidence score: The confidence level of each diagnosis result.

[0060] Multimodal data: Combined with relevant medical record data to provide reference opinions.

[0061] The feedback report will be sent to doctors in real time through system interfaces (such as hospital information system HIS) to help them make more accurate decisions.

[0062] Step S60: store the recognition results and related image data in the cloud to provide support for subsequent analysis.

[0063] In this step, the system stores the identified medical imaging diagnosis results and related imaging data for subsequent analysis, retrieval, and clinical decision support. Cloud storage not only ensures efficient data management and security, but also enables data sharing and collaborative analysis between different medical institutions, thereby providing valuable data support for future diagnosis.

[0064] In the above steps, the system has completed the real-time recognition of medical images and obtained the diagnosis results. These recognition results, including the diagnosis category, confidence, heat map data, etc. of each case, need to be stored systematically. All diagnosis results and related imaging data will be encrypted and uploaded to the cloud storage system. Cloud storage has high availability and redundancy mechanisms to ensure the security and consistency of data when used across devices and platforms. The upload process includes the following steps:

[0065] Data encryption and transmission: All data is encrypted before transmission to ensure that sensitive information (such as patient privacy) is effectively protected.

[0066] Data storage: Data storage adopts a distributed storage architecture to ensure reliable storage and fast access to large-scale data.

[0067] The goal of cloud storage is to ensure the persistence and efficiency of data while facilitating access, sharing and analysis at any time. Data stored in the cloud can not only be queried by doctors, but can also be used for subsequent statistical analysis, model training and performance optimization. For subsequent analysis, the system will retrieve stored case data based on specific query conditions (such as disease type, imaging characteristics, doctor feedback, etc.) and generate corresponding analysis reports. Through data analysis, the system can generate trend forecasts, clinical recommendations, and early diagnosis models for diseases based on big data. As the system is continuously used and improved, the data in the cloud storage will be continuously updated as new data is added. The system will continuously optimize existing models and analysis algorithms based on new diagnostic results and feedback, thereby continuously improving diagnostic accuracy and efficiency.

[0068] Embodiment 2

[0069] like Figure 2 As shown, Embodiment 2 of the present application provides a medical image recognition system integrating deep learning and big data algorithms, including:

[0070] Data collection module 21: used to collect medical imaging data from multiple medical institutions;

[0071] The data acquisition module is the first step of the entire medical image recognition system. Its main function is to collect medical image data from multiple medical institutions to ensure that the acquired data is diverse and comprehensive. The basic function of the data acquisition module is to obtain image data from the data system of medical institutions. These data are not limited to X-rays, CT scan images, MRI images, ultrasound images and other types. In addition, the collected image data includes not only the image itself, but also metadata related to the image, such as the time of image shooting, equipment model, basic information of the patient (such as age, gender, medical history, etc.), image annotation information, etc. These metadata are crucial for subsequent data processing and model training.

[0072] This system supports access to multiple data sources to ensure that image data can be collected from different types of medical institutions and equipment, including: hospital information system (HIS), picture archiving and communication system (PACS), electronic health record system (EHR). Medical image data has many different formats, and the data acquisition module needs to be able to support image files in these different formats, including: DICOM format (digital medical imaging and communication standard), JPEG, PNG and other common image formats and some non-standardized image files (such as the standard image format used by hospitals) also need to be recognized and processed by the system.

[0073] In order to ensure the efficiency and security of data when it arrives at the system from multiple medical institutions and equipment, the data acquisition module supports multiple data transmission protocols. Since medical imaging data usually involves the patient's personal privacy information, the data acquisition module strictly abides by relevant laws and regulations to ensure the security and privacy protection of patient data. All data in the data transmission and storage process must be encrypted using a strong encryption algorithm (such as AES-256) to ensure the confidentiality of the data; only authorized personnel can access the data acquisition system, and the system should have strict user authentication and access control mechanisms; during the processing process, the patient's privacy information (such as name, ID number, etc.) should be desensitized to ensure that the data will not be leaked.

[0074] The data acquisition module also supports two modes of automatic and manual acquisition: automatic acquisition. For conventional imaging data, the system can automatically acquire the data according to set rules. For example, by connecting with the PACS system, the latest imaging data can be automatically obtained from the hospital's imaging library. Manual acquisition. For special cases or emergency situations, users can manually upload imaging data to the system. Manually uploaded imaging data also needs to undergo system security verification and format conversion.

[0075] To ensure the quality of the data, the data acquisition module performs a preliminary quality check on the collected image data and removes image data with serious problems, such as blurry, damaged or incomplete images. After the data acquisition is completed, all image data will be stored in the local database or cloud storage system.

[0076] Data preprocessing module 22: used for standardizing image data;

[0077] The data preprocessing module is responsible for performing a series of operations to standardize medical imaging data, including image enhancement, denoising, image normalization, etc. The goal of the operation is to optimize the quality of the imaging data to make it suitable for the training and prediction of deep learning models.

[0078] Image enhancement is the key to data preprocessing. It aims to enhance the key information in the image by transforming and modifying the image, making the image features more prominent and facilitating subsequent feature extraction and model training. In order to improve the visualization of the image, a comprehensive image enhancement function E is used to make the image details clearer by adjusting the contrast, brightness and saturation. The definition of the enhancement function is as follows: Where I(x,y) represents the pixel value of the original image; α represents the contrast adjustment parameter, which controls the enhancement strength; μ represents the mean value of the image; β is the brightness adjustment parameter; k represents the domain range, which is used to calculate the local average; γ represents the saturation adjustment parameter. It is used to dynamically adjust different parameters and enhance according to the overall characteristics of the image to ensure the visualization of details.

[0079] Medical imaging data is often interfered by noise, which may come from the image acquisition device, transmission process or external environment. Denoising can effectively reduce the impact of noise on image quality, thereby avoiding the learning of irrelevant information during model training. In order to reduce the noise in the image, a denoising function D based on wavelet transform is used. This function combines local features and global structure and is defined as follows: Where n represents the number of wavelet transform layers; W j represents the j-th layer wavelet coefficient, which represents information of different frequencies; (x0, y0) represents the center coordinate of the image; σ represents the parameter that controls the denoising intensity, and a smaller value will lead to a stronger denoising effect. Through this formula, the edge features of the image can be effectively retained while denoising.

[0080] To ensure the uniformity of the image, the image pixel values ​​are normalized to between 0 and 1 using the following normalization formula: Among them, S represents the standardized pixel value; I(x, y) represents the pixel value of the original image; μ local Represents the mean of the local area of ​​the image, which can improve the retention of local features; σglobal Represents the global standard deviation of the image, which is used to ensure the overall consistency of the data; ∈ is a small constant to prevent division by zero; ∑ i,j I(x+i,y+j) represents the sum of all pixels in the local window centered at pixel (x,y); N local Represents the number of local pixels, used to calculate the local mean.

[0081] Model training module 23: used to build and train deep learning models;

[0082] The model training module is the core component of the entire medical image recognition system. It is responsible for building and training deep learning models, extracting effective features from image data, and optimizing model parameters through large-scale data training.

[0083] In this module, a deep learning model is constructed using a convolutional neural network, and adaptive feature extraction is performed in combination with a big data algorithm. Traditional convolutional neural networks extract image features through fixed convolution kernels and pooling operations, but in practical applications, the diversity and complexity of image features require the model to automatically adapt and optimize the feature extraction process. This paper proposes an adaptive feature extraction mechanism that combines adaptive convolution kernels, dynamic feature selection, and group learning strategies to improve the performance of the model in medical image recognition. The specific adjustments are as follows:

[0084] Adaptive convolution kernel adjustment: In traditional convolutional neural networks, the size and shape of the convolution kernel are usually fixed. However, due to the large differences in features between different regions in medical images, fixed convolution kernels often cannot fully capture all important information. Therefore, this module proposes an adaptive convolution kernel adjustment mechanism that enables the convolution kernel to automatically adjust its size and shape according to the local features of the input image. Specifically, the size and shape of the convolution kernel S L It is dynamically generated based on the local statistical characteristics of the image data. For example, image segmentation or clustering analysis can be used to determine the different regions and features of the image. The output F of the convolutional layer L (x,y) is represented as: Among them, N L is the number of convolution kernels used in the Lth convolutional layer; F L (x, y) represents the output feature map of the Lth layer; W L,i (I(x,y),S L ) represents the weight of the i-th convolution kernel in the L-th layer, which is based on the current image I(x, y) and the feature S of the specific layer L L Dynamically generated; S L Represents the shape and size of the convolution kernel, which is adjusted according to the local features of the image; F L-1(x,y) represents the output feature map of the previous layer. In this way, the convolution kernel can be adaptively adjusted to extract local features for different regions and enhance the ability to capture image details.

[0085] Dynamic feature selection and weight update: In image data, not all features contribute equally to the classification task. Traditional convolutional neural networks process features uniformly after extracting them at each layer. However, in a big data environment, the number and dimensions of features are often very large. Therefore, this module introduces a dynamic feature selection and weighting mechanism, which selects and updates features based on their classification contribution in a weighted manner at each layer. Each feature F i The weight α i It will be dynamically adjusted according to its contribution to the classification task. Specifically, the final optimized feature can be expressed as: Where M is the total number of features extracted; F optimized is the final feature representation after weighting; F i represents the i-th extracted feature; α i Represents the weight coefficient of the i-th feature, which is dynamically adjusted according to its importance during the training process. The weight α i The update is achieved through the back-propagation algorithm, which optimizes each feature based on its impact on model performance. This mechanism enables the model to automatically learn which features are more important for the classification task, thereby improving the accuracy of the model.

[0086] Group learning feature fusion: In medical image recognition, image data usually has a complex structure, and a single feature extraction strategy may not be able to fully capture all information. Therefore, this module combines the group learning strategy in big data algorithms, using distributed computing and cluster learning to extract features from multiple sub-networks and fuse these features into a unified feature representation. Assume that the network is divided into multiple sub-networks N1, N2, ..., N k , each sub-network independently extracts features from different subsets of the data. Each sub-network N j The extracted feature is F j , the final fused feature F final It can be expressed as: Where K represents the number of subnetworks; F final represents the final fused feature representation; F j represents the features extracted by the jth sub-network; β j represents the weight coefficient of the jth sub-network, indicating the importance of the sub-network in the final feature fusion. These weight coefficients β jIt is dynamically updated through group learning to ensure that the final fused features can effectively represent the common features of all image data while reducing possible redundant information. Through adaptive convolution kernels, dynamic feature selection and group learning fusion, this model can continuously optimize the feature extraction process according to the characteristics of image data.

[0087] Furthermore, an ensemble learning method is used to train the deep learning model and optimize the model parameters. The convolutional neural network is initialized and trained using the training set. During the training process, the gradient descent method is used to optimize the loss function and gradually update it in combination with the adaptive learning rate. Assume that the loss function is The model parameter is θ, and the parameter is optimized by gradient descent. The formula is as follows: Among them, θ t is the model parameter at the current moment; τ t is the learning rate at the current moment, which is dynamically adjusted according to the training progress; is the gradient of the loss function with respect to the parameters. In order to effectively avoid the local minimum problem, stochastic gradient descent is used in this module, which randomly extracts part of the data for gradient calculation at each update, thereby accelerating the training process and avoiding falling into the local minimum. In order to accelerate convergence and improve training results, the learning rate τ t It will be adaptively adjusted with the training round t. The following adaptive learning rate adjustment formula is used to ensure that the model is optimized with different learning rates at different stages of training: Among them, τ0 is the initial learning rate; γ is the decay factor, which controls the decay speed of the learning rate; t is the current training round. The function of this formula is to gradually reduce the learning rate as the training progresses, so as to ensure that the model can be adjusted more finely when approaching the optimal solution to avoid over-adjustment. In order to avoid over-training of the model, a dynamic early stopping mechanism is introduced to determine when to stop training by calculating the gain function during the training process. The gain function is defined as: in, and are the loss function values ​​of the current and previous rounds of training, G t It measures the rate of decrease of the loss function during the training process. If the value of the gain function is less than a preset threshold in N consecutive rounds of training, the training is stopped to prevent overfitting.

[0088] After the model training and optimization are completed, in order to further optimize the model performance, multiple optimization results are fused through the weighted average method to obtain the final prediction output. The fusion formula is: in, is the final prediction result; K represents the number of sub-models involved in the fusion; is the predicted value of the i-th model; αi The weight of the model is dynamically adjusted based on the training performance of the model; is the average of all predicted values; β is the adjustment factor to control the impact of prediction deviation.

[0089] Real-time recognition module 24: used for real-time recognition of input medical images;

[0090] In this module, the input medical images are processed and recognized in real time, the corresponding diagnosis results are generated, and the results are fed back to the doctor in a timely manner through the feedback mechanism. Through this process, it is ensured that medical decisions can be made in the shortest time and effective support is provided to improve the efficiency and accuracy of diagnosis and treatment.

[0091] When medical images are input into the system, they are first preprocessed to ensure that the input data meets the requirements of the model. The preprocessed medical images are then input into the trained deep learning model for real-time feature extraction and prediction. Based on the convolutional neural network architecture, the model extracts key features from the image through multiple convolutional layers, pooling layers, and fully connected layers. Once the model completes the inference of the image, the output diagnosis result will be transmitted to the doctor through a real-time feedback mechanism. To enhance the effectiveness of the feedback, the model will not only provide simple classification results, but also come with corresponding confidence scores. Based on this feedback information, doctors can quickly understand the diagnostic results of the image and its credibility, and make further clinical decisions. In order to improve doctors' understanding of the diagnostic results and decision-making efficiency, the system will also present the recognition results in a visual way and use a heat map display method to mark the key areas in the image with the predicted results.

[0092] To further improve the accuracy of diagnosis and support doctors’ decision-making, the system will combine multimodal data (such as medical images, medical records, etc.) to generate a comprehensive feedback report. The comprehensive report includes the following:

[0093] Diagnostic results: The system's analysis results of medical images, including lesion type, location, etc.

[0094] Confidence score: The confidence level of each diagnosis result.

[0095] Multimodal data: Combined with relevant medical record data to provide reference opinions.

[0096] The feedback report will be sent to doctors in real time through system interfaces (such as hospital information system HIS) to help them make more accurate decisions.

[0097] Data storage module 25: used to store recognition results and image data in the cloud.

[0098] In this module, the system stores the identified medical imaging diagnosis results and related imaging data for subsequent analysis, retrieval and clinical decision support. Cloud storage not only ensures efficient data management and security, but also enables data sharing and collaborative analysis between different medical institutions, thus providing valuable data support for future diagnosis.

[0099] The goal of cloud storage is to ensure the persistence and efficiency of data while facilitating access, sharing and analysis at any time. Data stored in the cloud can not only be queried by doctors, but can also be used for subsequent statistical analysis, model training and performance optimization. For subsequent analysis, the system will retrieve stored case data based on specific query conditions (such as disease type, imaging characteristics, doctor feedback, etc.) and generate corresponding analysis reports. Through data analysis, the system can generate trend predictions, clinical recommendations, and early diagnosis models for diseases based on big data. As the system is continuously used and improved, the data in the cloud storage will be continuously updated as new data is added. The system will continuously optimize existing models and analysis algorithms based on new diagnostic results and feedback, thereby continuously improving diagnostic accuracy and efficiency.

[0100] Corresponding to the above embodiment, an embodiment of the present invention provides a computer storage medium, including: at least one memory and at least one processor;

[0101] The memory is used to store one or more program instructions;

[0102] A processor is used to run one or more program instructions to execute a medical image recognition method that integrates deep learning and big data algorithms.

[0103] Corresponding to the above embodiments, an embodiment of the present invention provides a computer-readable storage medium, which contains one or more program instructions, and the one or more program instructions are used by a processor to execute a medical image recognition method that integrates deep learning and big data algorithms.

[0104] The embodiments disclosed in the present invention provide a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are executed on a computer, the computer executes the above-mentioned medical image recognition method integrating deep learning and big data algorithms.

[0105] In the embodiment of the present invention, the processor may be an integrated circuit chip having the ability to process signals. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0106] The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor for execution, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The processor reads the information in the storage medium and completes the steps of the above method in combination with its hardware.

[0107] The storage medium may be a memory, which may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0108] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0109] The volatile memory may be a random access memory (RAM) which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus DRAM (DRRAM).

[0110] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0111] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the present invention can be implemented using a combination of hardware and software. When software is used, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any media that facilitates the transmission of computer programs from one place to another. Storage media can be any available media that can be accessed by general or special-purpose computers.

[0112] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A medical image recognition method integrating deep learning and big data algorithms, characterized in that: The following steps are involved: Collect medical imaging data from multiple medical institutions and build a large-scale medical imaging database; Standardized processing of collected medical images, including image enhancement, denoising, and resizing; Based on the processed data, a deep learning model is constructed using a convolutional neural network and feature extraction is performed in combination with a big data algorithm; Use ensemble learning methods to train deep learning models and optimize model parameters; After the model training is completed, the input medical images are recognized in real time and the recognition results are fed back to the doctor; The recognition results and their related image data are stored in the cloud to provide support for subsequent analysis.

2. A medical image recognition method integrating deep learning and big data algorithms as claimed in claim 1, characterized in that: The deep learning model adopts a convolutional neural network structure, and the model can be adaptively adjusted according to different types of medical images.

3. The medical image recognition method integrating deep learning and big data algorithm as claimed in claim 1, characterized in that: The data preprocessing steps include image enhancement, denoising and standardization to ensure that the model can handle diverse data inputs during training.

4. The medical image recognition method integrating deep learning and big data algorithm as claimed in claim 1, characterized in that: During the model training process, the deep learning model is trained by adopting an integrated learning method to optimize the model parameters.

5. The medical image recognition method integrating deep learning and big data algorithm as claimed in claim 1, characterized in that: The real-time recognition also includes providing the confidence value of the recognition result to the doctor to assist in clinical judgment.

6. The medical image recognition method integrating deep learning and big data algorithm as claimed in claim 1, characterized in that: The system can generate recognition reports for different disease categories and analytical charts of model performance to help doctors understand the recognition capabilities of the model and its effectiveness in practical applications.

7. The medical image recognition method integrating deep learning and big data algorithm as claimed in claim 1, characterized in that: The cloud storage has high security and data encryption mechanisms to protect the privacy of medical imaging data, and supports regular data backup and recovery functions to ensure the security and integrity of the data.

8. A medical image recognition system integrating deep learning and big data algorithms, characterized in that: The system comprises: A data collection module for collecting medical imaging data from multiple medical institutions; Data preprocessing module, used to standardize image data; Model training module, used to build and train deep learning models; A real-time recognition module is used to perform real-time recognition of input medical images; The data storage module is used to store the recognition results and image data in the cloud.

9. A computer storage medium comprising: at least one memory and at least one processor; The memory is used to store one or more program instructions; A processor, used to run one or more program instructions to execute a medical image recognition method integrating deep learning and big data algorithms as described in any one of claims 1-7.

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