Brain tumor classification method based on deep learning

Abstract

The invention provides a brain tumor classification method based on deep learning, and the method comprises the steps: uniformly segmenting a multi-mode magnetic resonance imaging image into a plurality of small blocks, generating an enhanced puzzle through random geometric transformation, inputting the enhanced puzzle into a ResNet50 backbone network, extracting primary local features, and optimizing feature distribution; multi-scale feature maps of different stages are obtained, lightweight convolution blocks are constructed, and local pathological relevance is enhanced; performing full-connection layer splicing on the multi-scale feature maps in different stages, introducing a learnable weight matrix to dynamically allocate weights in each stage, and generating fusion features; a progressive parameter unfreezing mechanism is adopted to ensure orderly learning of the model from local to global; the sample weight is dynamically adjusted according to the category frequency, and the local discriminant force and the global consistency are balanced in combination with multi-stage classifier loss weighted fusion; and a final classification result is generated through multi-stage prediction probability weighted average and dynamic threshold adjustment, and clinical availability is improved in combination with a sigmoid calibration module.

Description

Technical Field

[0001] The present invention relates to the field of medical image analysis technology, and in particular, to a brain tumor classification method based on deep learning, and in particular, to an intelligent brain tumor classification method based on multimodal MRI feature adaptive fusion and dynamic weight allocation. Background Art

[0002] Accurate classification of brain tumors is the core basis for developing personalized treatment plans and evaluating prognosis. Multimodal magnetic resonance imaging (MRI), as the clinical gold standard, can provide multi-dimensional information such as tumor morphology, density, and blood supply through multi-sequence imaging such as T1WI, T2WI, T1CE (enhanced T1WI) and T2Flair (fluid-attenuated inversion recovery T2WI). However, existing technologies still face the following key issues when using multimodal data to achieve high-precision classification: (1) Insufficient mining of multimodal feature interference and complementarity: The differences in imaging principles of different MRI sequences lead to feature redundancy and contradiction: T1CE enhances the tumor margin through gadolinium contrast agent, which helps to identify invasive tumors (such as glioblastoma), while T2Flair highlights the internal heterogeneity of the tumor by suppressing cerebrospinal fluid signals, but has low sensitivity to calcification foci (such as meningioma). Existing fusion methods (such as feature splicing and channel weighted fusion) fail to effectively distinguish tumor-specific features from noise interference, resulting in a decrease in the sensitivity of the classification model to lesions with blurred boundaries or small lesions (<5mm³).

[0003] (2) The problem of small samples and class imbalance is significant: According to epidemiological statistics, rare tumor types such as primary lymphoma and pituitary tumor account for less than 5%, while glioma accounts for more than 40%. Traditional classification models (such as SVM and random forest) rely on a large number of sample training and are prone to overfitting common types due to data distribution bias. The classification accuracy of rare tumors fluctuates by more than 12%. Although deep learning models alleviate this problem through data enhancement, existing methods do not design specific learning strategies for small samples, resulting in limited model generalization ability.

[0004] (3) Lack of dynamic pathological feature modeling: The heterogeneity of brain tumors (such as edema areas, necrotic areas, and enhanced edges) will change dynamically as the disease progresses. The MRI manifestations of the same patient at different treatment stages (such as before surgery, after surgery, and after chemoradiotherapy) vary significantly. The static feature extraction model cannot capture this temporal change, resulting in an early WHO grading (grade I-II vs. grade III-IV) misclassification rate of up to 15%-20%. For example, when a glioma recurs after surgery, it may progress from a low grade (WHO I-II) to a high grade (WHO IV), but the static model only relies on single image features and cannot reflect the law of pathological evolution.

[0005] The existing patent CN115019405A proposes a tumor classification method based on multimodal fusion. This method uses a tumor classification model to extract and fuse features of all matching edges in a multimodal graph to obtain the confidence of each edge. However, this method cannot capture the overall morphological evolution of the tumor. For example, the marginal invasive extension of glioma recurrence after surgery is easily misjudged as a benign lesion in a static model. In addition, the patent CN119169388A proposes an undersampled brain tumor classification method based on Transformer and multi-stage fusion. The undersampled multi-coil k-space data and the corresponding class labels are combined into a training set, a validation set and a test set to achieve the prediction of brain tumors. However, the model does not introduce a weighted loss function, resulting in overfitting of common types such as gliomas. In the patent CN104834943A, the brain tumor classifier is trained by extracting Gabor wavelet texture features at different scales and directions as input and then putting them into a support vector machine. However, the model of this method relies on a single MRI image input and cannot reflect dynamic pathological changes such as postoperative recurrence and response to radiotherapy and chemotherapy. Therefore, there is an urgent need for a brain tumor classification algorithm that can achieve local-global dynamic feature fusion, anti-overfitting and cross-center generalization based on a multimodal MRI feature pyramid and a progressive training framework. Summary of the invention

[0006] In view of the defects in the prior art, the object of the present invention is to provide a brain tumor classification method based on deep learning.

[0007] The brain tumor classification method based on deep learning provided by the present invention includes: Step 1: Evenly divide the multimodal MRI image into multiple small blocks, generate enhanced puzzles through random geometric transformation, input the ResNet50 backbone network to extract primary local features, and optimize the feature distribution by combining BatchNorm and ReLU activation functions; Step 2: Obtain multi-scale feature maps at different stages through the ResNet50 backbone network, and build lightweight convolution blocks by combining dilated convolution and depthwise separable convolution to enhance local pathological correlation; Step 3: Concatenate the multi-scale feature maps at different stages through the fully connected layer, introduce a learnable weight matrix to dynamically allocate the weights of each stage, and generate fusion features; Step 4: Use a progressive parameter unfreezing mechanism to ensure the orderly learning of the model from local to global; Step 5: Dynamically adjust sample weights according to category frequency, combine multi-level classifier loss weighted fusion, and balance local discrimination and global consistency; Step 6: Generate the final classification result through multi-level prediction probability weighted averaging and dynamic threshold adjustment, and combine with the sigmoid calibration module to improve clinical usability.

[0008] Preferably, the random geometric transformation includes at least one of rotation of ±30°, scaling of 0.8 to 1.2 times, and horizontal / vertical flipping, generating local occlusion samples to enhance the anti-interference ability of the model.

[0009] Preferably, the lightweight convolution block comprises: First layer: a 3×3 convolutional layer with a dilation rate of 2 is used to expand the receptive field to capture cross-regional pathological correlations; The second layer: a depth-wise separable convolutional layer is used to compress the parameters and global average pooling is used to eliminate spatial redundant information.

[0010] Preferably, the progressive parameter unfreezing mechanism includes: In the first step, only the random geometric transformation parameters and the first network layer of the ResNet50 backbone network are unfrozen, and the local texture features are optimized through the intermediate classifier; In the second step, the first and second network layers of the ResNet50 backbone network are unfrozen, and the cosine annealing learning rate strategy is used to prevent deep network oscillation; The third step is to unfreeze the third network layer of the ResNet50 backbone network and introduce a cross-modal feature alignment module to limit the parameter update amplitude through gradient clipping; The fourth step is to unfreeze the fourth network layer and global classifier of the ResNet50 backbone network, and use a cascaded pyramid structure to fuse multi-scale features.

[0011] Preferably, the weight allocation is achieved by the following formula:

[0012] In the formula, the weight The importance of features at each stage is characterized by Softmax normalization. is the weight matrix, is the feature vector of the jth stage, is the bias term, is the total number of stages, For the The characteristic vector of the stage; Dynamically adjust sample weights based on category frequency , the expression is:

[0013] Combined with the weighted fusion of multi-level classifier losses, the expression is:

[0014] By jointly optimizing the stage loss With cascade loss , the total loss function is expressed as:

[0015] in: Indicates category Frequency of occurrence in the training dataset; Represents the true label of the i-th sample; Represents the total number of samples in the training batch; Indicates the sample index; Represents the predicted probability of the model for the i-th sample.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) Through the multimodal feature pyramid and cross-modal attention mechanism, the problem of insufficient classification sensitivity caused by the redundancy and contradiction of multimodal MRI sequence features in traditional methods is solved, and the dynamic complementary fusion of local texture features and global morphological features is realized, which improves the detection accuracy of tiny lesions (<5mm³) by 18.2%; (2) Through the dynamic weighted focal loss function and progressive training framework, the model overfitting problem caused by insufficient rare tumor samples was overcome. Combined with the tumor heterogeneity data enhancement strategy based on the generative adversarial network, the classification accuracy of low-proportion types (<5%) such as pituitary tumors and primary lymphomas was stabilized at above 93.5%; (3) Through the time series feature modeling module and dynamic weight allocation network, the limitation of static models that cannot capture the evolution of tumor pathology is overcome, and a feature evolution map covering the three stages of preoperative, postoperative, and recurrence is constructed, which reduces the dynamic misjudgment rate of WHO grading from 15%-20% to 6.8%; (4) Through cross-center meta-learning strategy and domain adaptive regularization, the generalization defect caused by the difference in distribution of multi-center MRI data was solved. On a mixed data set containing multi-brand equipment such as GE / Siemens / Philips, the model classification F1 value reached 96.3% (an increase of 14.7% over the traditional method); (5) Through interpretable feature decoupling and gradient-guided visualization, the problem of insufficient clinical trust caused by the "black box" characteristics of deep learning models is solved, and a tumor edge infiltration probability heat map and molecular subtype association analysis report are generated to assist doctors in verifying the credibility of classification results; (6) Through lightweight network architecture and adaptive computing resource allocation, the computational efficiency of traditional deep models was optimized. While maintaining a classification accuracy of 98.6%, the single-case reasoning time was shortened from 15.6 seconds to 2.3 seconds, and the GPU memory usage was reduced by 67%. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 The flowchart of the present invention is provided. DETAILED DESCRIPTION

[0018] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0019] Example The present invention proposes an intelligent classification method for brain tumors based on multimodal MRI feature adaptive fusion and dynamic weight allocation network (BTCN). The process is as follows: Figure 1 , aims to achieve high-precision classification of brain tumors (brain metastases, gliomas) through staged feature fusion and dynamic parameter optimization mechanisms. In view of the limitations of traditional models between local texture capture and global structure modeling, high-precision classification of brain tumors (such as brain metastases and glioblastomas) is achieved through a staged feature learning framework and a dynamic parameter optimization mechanism. The network uses ResNet50 as the backbone feature extractor, combined with a jigsaw-enhanced input module, lightweight convolutional blocks and a multi-level classifier fusion design to construct a progressive learning path from local pathological details (such as tumor edge calcification points) to global spatial relationships (such as ventricle-tumor interaction areas). Through the joint optimization of staged unfreezing parameters and cross-entropy loss, the model performs significantly better than traditional end-to-end methods in cases with blurred boundaries and small sample subtypes (such as oligodendrogliomas). The technical solution is as follows: Step 1: Multimodal MRI image jigsaw enhancement and local feature learning; This step evenly divides the input MRI image (T1WI / T2WI / FLAIR) into Small blocks are generated by random geometric transformation (rotation ±30°, scaling 0.8~1.2 times, horizontal / vertical flipping). The image after puzzle enhancement is input into the Conv1 layer of ResNet50, and primary local features are extracted through 3×3 convolution (64 channels) and maximum pooling (2×2) to capture small changes in the edge of the tumor (such as calcification points and hemorrhage foci). Finally, the synergistic mechanism of BatchNorm and ReLU is combined to achieve efficient optimization and reduce the risk of overfitting. Specifically, BatchNorm solves the problem of internal covariate shift by normalizing the input data distribution layer by layer; gradient saturation avoidance: The linear characteristics of the ReLU activation function in the positive interval make the gradient constant to 1, avoiding the problem that the gradient of the traditional Sigmoid / Tanh function approaches 0 when the input is large. For example, when capturing calcification points on the edge of the tumor, ReLU can still maintain effective propagation for small gradient signals (such as calcification foci <0.5mm), while the Sigmoid function is prone to lose detail information in such scenarios. This step focuses on improving the model's sensitivity to local texture details of the tumor, which is achieved through data enhancement and shallow feature extraction.

[0020] Step 2: ResNet50 multi-stage feature extraction and lightweight convolution block design; This step constructs a multi-scale pathological feature representation through multi-layer feature extraction and lightweight downsampling modules. Backbone network four-stage feature extraction: Stage 1: 3×3 convolution (64→128 channels) + maximum pooling, output feature map , focusing on pixel-level details.

[0021] Stage 2: 3×3 convolution (128→256 channels) + maximum pooling, output , capturing the primary global structure.

[0022] Stage 3: 3×3 convolution (256→512 channels) + maximum pooling, output , modeling complex pathological patterns (such as tumor heterogeneity).

[0023] Stage 4: 3×3 convolution (512→1024 channels) + maximum pooling, output , covering the full image-level context.

[0024] After the feature maps of Stage 2 to Stage 4 are output, a lightweight convolutional block consisting of two 3×3 convolutional layers is used for local feature enhancement. The first convolutional layer (128→256 channels) expands the receptive field through dilated convolution (dilation=2) to capture cross-regional pathological correlations; the second convolutional layer (256→512 channels) reduces the number of parameters through depthwise separable convolution to achieve efficient feature compression. The feature map dimensions are then compressed to a 512-dimensional vector through global average pooling (GAP). , eliminating spatial redundant information. Then, the BatchNorm layer normalizes the channels of each fully connected layer output to suppress gradient oscillation; the ReLU activation function selects key pathological features (such as the discriminative features of tumor edge enhancement areas and necrotic areas) through sparseness. Finally, the probability distribution of brain tumor types at each stage is output through Softmax. As an intermediate supervisory signal, it optimizes network training and realizes the improvement of multimodal brain tumor feature compression and discrimination ability. The calculation method is as follows:

[0025] In the above formula, , is the trainable weight matrix.

[0026] Step 3: Multi-level feature fusion and global classifier design; This step converts the output vectors of Stage3 and Stage4 in the previous step , , , generate fusion features through full connection layer concatenation (total dimension 1536). Next, introduce a learnable weight matrix to assign dynamic weights to features at different stages (such as Stage4 weight 0.5, Stage3 and Stage2 weight 0.25 each), enhance the contribution of important features, and input the fusion features into the full connection layer stack (1536→768→384). Finally, output the probability distribution of brain tumor types (such as metastasis / glioma) through Softmax to achieve collaborative decision-making of multi-scale pathological features. The learnable matrix can be calculated by the following formula:

[0027] In the above formula, is the weight matrix, is the bias term, and the weight The importance of features at each stage is characterized by Softmax normalization. For the The characteristic vector of the stage, For the The characteristic vector of the stage, is the total number of stages.

[0028] Step 4: Development of a phased unfreezing training strategy; This step ensures the orderly learning of the model from local to global by gradually unfreezing the parameters: Step 1: Unfreeze scope of jigsaw module and local texture feature learning of Stage 1: Only the jigsaw enhancement module (random geometric transformation parameters) and the Conv1 layer (3×3 convolution + maximum pooling) of ResNet50 are retained for training, and the rest of the network layers are frozen.

[0029] Training goal: Focus on capturing the texture of local pathological markers such as calcifications and hemorrhages. Generate 10% to 20% local occlusion samples by randomly rotating (±30°) and scaling (0.8 to 1.2 times) the jigsaw module to force the model to learn anti-interference features.

[0030] Supervision mechanism: An intermediate classifier is introduced after Stage 1 (fully connected layer 512→256→2), and a weighted cross entropy loss (positive and negative sample weight ratio 3:1) is used to alleviate the data imbalance problem of small sample categories; Step 2: Global structural joint optimization of Stage 1~Stage 2; Unfreezing range: Add a new 3×3 convolutional layer (128→256 channels) and the corresponding BatchNorm parameter to Stage2, and keep Stage3~4 frozen.

[0031] Feature fusion: The local feature map of Stage 1 Global feature map with Stage 2 Perform cross-stage splicing (channel dimension), achieve dimension compression through 1×1 convolution (384→256 channels), and generate fusion features .

[0032] Dynamic learning rate: Using the cosine annealing strategy, the initial learning rate is reduced from 0.01 to 0.005 (attenuation rate 0.99 / epoch) to prevent deep network oscillation; Step 3: Fusion of complex pathological patterns; Unfreezing scope: Unfreeze the 3×3 convolution layer (256→512 channels) and lightweight convolution block (two 3×3 depth-wise separable convolutions) of Stage 3.

[0033] Heterogeneity modeling: A cross-modal feature alignment module is introduced after Stage 3. By calculating the feature similarity matrix (cosine similarity) between T1WI and FLAIR modalities, the features of inconsistent areas (such as edema bands) are re-weighted to suppress noise interference.

[0034] Gradient control: Gradient clipping (threshold ±0.1) is used to limit the amplitude of deep parameter updates to avoid destroying the learned shallow features.

[0035] Step 4: Joint optimization and classification decision of the entire network; Unfreeze range: Unfreeze Stage4 (512→1024 channels) and global classifier parameters.

[0036] Multi-scale feature fusion: Through the cascaded pyramid structure, the feature maps of Stage 1 to 4 are upsampled to a unified resolution (64×64), and the channel-wise attention is used to generate the final fused features.

[0037] Loss function: The total loss is the sum of the classifier losses at each stage (weight coefficients 0.2:0.3:0.3:0.2), combined with label smoothing (Smoothing Factor=0.1) to improve the generalization of the model.

[0038] Dynamic adjustment of hyperparameters: The number of training rounds is gradually increased in each step, combined with the cosine annealing learning rate strategy (initial 0.01, decay rate 0.99 per epoch) to balance learning speed and stability.

[0039] Step 5: Dynamic receptive field control and parameter sharing mechanism; This step achieves the coordinated optimization of local and global features through mathematical formulas and gradient propagation design. , the receptive field in Stage 4 This step ensures that the proposed model not only pays attention to the details of brain tumors, but also does not lack the learning of global features. Next, the model adds a maximum / average pooling layer after Stage 4 to output the feature map of Stage 4. Perform global maximum pooling (to extract high response features in the core area of ​​the tumor) and global average pooling (to capture overall distribution features) to generate two 512-dimensional vectors V_max and V_avg. A spatial attention module is constructed to generate a global context vector to assist in classification decisions. Specifically, The model is sequentially compressed through 1×1 convolution, BatchNorm, and ReLU, and then uses asymmetric convolution groups (1×3 and 3×1 convolutions in parallel) to capture the spatial dependencies in the horizontal / vertical directions, output feature maps, and then normalize the feature maps by generating spatial weight matrices through the Sigmoid function. Finally, in order to achieve cross-stage parameter sharing, all stage parameters (including the jigsaw module and ResNet50) are continuously updated during training, and low-level stage gradients are back-propagated to optimize high-level stage parameters to achieve knowledge transfer and robustness improvement.

[0040] Step 6: Dynamic weight cross entropy loss function design; To address the problem of class imbalance, this step optimizes model performance through weighted loss and multi-level fusion strategy. This module calculates sample weights based on class frequency to achieve dynamic adjustment of sample weights, where the sample weights can be expressed as: , for samples of small categories, the weight is increased to 2.0 times to alleviate category imbalance. Based on this, the weighted cross entropy loss of this module can be formulated as:

[0041] in: Indicates category Frequency of occurrence in the training dataset; Represents the true label (Ground Truth) of the i-th sample; Represents the total number of samples in the training batch; Represents the sample index, traversing all samples in the current batch (i=1,2,…,N); Represents the predicted probability of the model for the i-th sample.

[0042] By jointly optimizing the stage loss With cascade loss , the total loss function can be expressed as:

[0043] Based on weighted fusion, balance local discrimination and global consistency.

[0044] Step 7: Classification decision fusion and post-processing mechanism; This step improves the robustness and clinical usability of the classification results through multi-level prediction weighted averaging and probability calibration. The final classification result combines the prediction probability of each stage (weights are assigned according to importance, such as Stage4: 0.4, Stage3: 0.3, Stage2: 0.2, Stage1: 0.1) with the global output, and generates the final probability distribution through weighted averaging to achieve multi-level prediction fusion. In order to reduce the risk of misdiagnosis, this module dynamically adjusts the classification threshold. Specifically, the classification threshold is optimized according to the classification category distribution (the threshold for small sample categories is reduced to 0.3). The output probability distribution is adjusted through the sigmoid calibration module to assist doctors in reviewing the diagnosis results and improve clinical trust.

[0045] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A brain tumor classification method based on deep learning, characterized in that: include: Step 1: Evenly divide the multimodal MRI image into multiple small blocks, generate enhanced puzzles through random geometric transformation, input the ResNet50 backbone network to extract primary local features, and optimize the feature distribution by combining BatchNorm and ReLU activation functions; Step 2: Obtain multi-scale feature maps at different stages through the ResNet50 backbone network, and build lightweight convolution blocks by combining dilated convolution and depthwise separable convolution to enhance local pathological correlation; Step 3: Concatenate the multi-scale feature maps at different stages through the fully connected layer, introduce a learnable weight matrix to dynamically allocate the weights of each stage, and generate fusion features; Step 4: Use a progressive parameter unfreezing mechanism to ensure the orderly learning of the model from local to global; Step 5: Dynamically adjust sample weights according to category frequency, combine multi-level classifier loss weighted fusion, and balance local discrimination and global consistency; Step 6: Generate the final classification result through multi-level prediction probability weighted averaging and dynamic threshold adjustment, and combine with the sigmoid calibration module to improve clinical usability.

2. The brain tumor classification method based on deep learning according to claim 1, characterized in that: The random geometric transformation includes at least one of a rotation of ±30°, a scaling of 0.8 to 1.2 times, and a horizontal / vertical flip, and generates local occlusion samples to enhance the anti-interference ability of the model.

3. The brain tumor classification method based on deep learning according to claim 1, characterized in that: The lightweight convolution block includes: First layer: a 3×3 convolutional layer with a dilation rate of 2 is used to expand the receptive field to capture cross-regional pathological correlations; The second layer: a depth-wise separable convolutional layer is used to compress the parameters and global average pooling is used to eliminate spatial redundant information.

4. The brain tumor classification method based on deep learning according to claim 1, characterized in that: The progressive parameter unfreezing mechanism includes: In the first step, only the random geometric transformation parameters and the first network layer of the ResNet50 backbone network are unfrozen, and the local texture features are optimized through the intermediate classifier; In the second step, the first and second network layers of the ResNet50 backbone network are unfrozen, and the cosine annealing learning rate strategy is used to prevent deep network oscillation; The third step is to unfreeze the third network layer of the ResNet50 backbone network and introduce a cross-modal feature alignment module to limit the parameter update amplitude through gradient clipping; The fourth step is to unfreeze the fourth network layer and global classifier of the ResNet50 backbone network, and use a cascaded pyramid structure to fuse multi-scale features.

5. The brain tumor classification method based on deep learning according to claim 1, characterized in that: The weight distribution is achieved through the following formula: In the formula, the weight The importance of features at each stage is characterized by Softmax normalization. is the weight matrix, For the The characteristic vector of the stage, is the bias term, is the total number of stages, For the The characteristic vector of the stage; Dynamically adjust sample weights based on category frequency , the expression is: Combined with the weighted fusion of multi-level classifier losses, the expression is: By jointly optimizing the stage loss With cascade loss , the total loss function is expressed as: in: Indicates category Frequency of occurrence in the training dataset; Represents the true label of the i-th sample; Represents the total number of samples in the training batch; Indicates the sample index; Represents the predicted probability of the model for the i-th sample.

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