Brain tumor classification method based on deep learning

Abstract

The present invention provides a brain tumor classification method based on deep learning, comprising: evenly dividing a multimodal magnetic resonance imaging image into multiple small blocks, generating an enhanced puzzle through random geometric transformation, inputting the puzzle into a ResNet50 backbone network to extract primary local features and optimize feature distribution; obtaining multi-scale feature maps at different stages, constructing lightweight convolution blocks, and enhancing local pathology correlation; performing full-connection layer splicing on the multi-scale feature maps at different stages, introducing a learnable weight matrix to dynamically allocate weights at each stage, and generating fusion features; adopting a progressive parameter unfreezing mechanism to ensure orderly learning of the model from local to global; dynamically adjusting sample weights according to category frequency, combining multi-level classifier loss weighted fusion to balance local discrimination and global consistency; generating the final classification result through multi-level prediction probability weighted averaging and dynamic threshold adjustment, and combining a sigmoid calibration module to improve clinical usability.

Description

Technical Field

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

[0002] Accurate classification of brain tumors is the core basis for developing personalized treatment plans and assessing prognosis. Multimodal magnetic resonance imaging (MRI), the clinical gold standard, provides multidimensional information such as tumor morphology, density, and blood supply through multiple imaging sequences such as T1WI, T2WI, T1CE (enhanced T1WI), and T2Flair (fluid-attenuated inversion recovery T2WI). However, existing technologies still face the following key issues when achieving high-precision classification using multimodal data:

[0003] (1) Insufficient multimodal feature interference and complementarity mining: Differences in the imaging principles of different MRI sequences lead to feature redundancy and contradictions: T1CE enhances tumor margins through gadolinium contrast agents, which helps identify invasive tumors (such as glioblastomas), while T2Flair highlights internal tumor heterogeneity by suppressing cerebrospinal fluid signals, but has low sensitivity to calcifications (such as meningiomas). Existing fusion methods (such as feature splicing and channel weighted fusion) fail to effectively distinguish tumor-specific features from noise interference, resulting in reduced sensitivity of the classification model to lesions with blurred boundaries or small lesions (<5mm³).

[0004] (2) Small sample sizes and significant class imbalance: According to epidemiological statistics, rare tumor types such as primary lymphoma and pituitary tumors account for less than 5%, while gliomas account for over 40%. Traditional classification models (such as SVM and random forests) rely on large sample sizes for training and are prone to overfitting common types due to data distribution bias. Classification accuracy for rare tumors fluctuates by more than 12%. Although deep learning models alleviate this problem through data augmentation, existing methods do not design specific learning strategies for small samples, resulting in limited model generalization capabilities.

[0005] (3) Lack of dynamic pathological feature modeling: The heterogeneity of brain tumors (such as edema areas, necrotic areas, and enhanced edges) changes dynamically as the disease progresses. The MRI manifestations of the same patient at different treatment stages (such as preoperative, postoperative, and post-chemoradiotherapy) vary significantly. Static feature extraction models are unable to capture this temporal change, resulting in an early WHO grade (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 static models rely only on single-shot image features and cannot reflect the pathological evolution pattern.

[0006] Existing patent CN115019405A proposes a tumor classification method based on multimodal fusion. This method uses a tumor classification model to extract and fuse features from all matching edges in a multimodal graph to obtain the confidence level of each edge. However, this method cannot capture the overall morphological evolution of the tumor. For example, the expansion of invasive edges during postoperative glioma recurrence can be easily misclassified as benign lesions in a static model. In addition, patent CN119169388A proposes an undersampling brain tumor classification method based on Transformer and multi-stage fusion. This method combines undersampled multi-coil k-space data and corresponding class labels into training, validation, and test sets to predict brain tumors. However, this model does not introduce a weighted loss function, which leads to overfitting of common types such as gliomas. Patent CN104834943A extracts Gabor wavelet texture features at different scales and directions as input, which are then fed into a support vector machine to train a brain tumor classifier. However, this method model relies on a single MRI image input and cannot reflect dynamic pathological changes such as postoperative recurrence and response to chemoradiotherapy. 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

[0007] In view of the defects in the prior art, the purpose of the present invention is to provide a brain tumor classification method based on deep learning.

[0008] The brain tumor classification method based on deep learning provided by the present invention includes:

[0009] Step 1: The multimodal MRI image is evenly divided into multiple small blocks. The enhanced puzzle is generated through random geometric transformation. The primary local features are extracted by the ResNet50 backbone network. The feature distribution is optimized by combining BatchNorm and ReLU activation functions.

[0010] Step 2: Obtain multi-scale feature maps at different stages through the ResNet50 backbone network, and build a lightweight convolution block by combining dilated convolution and depthwise separable convolution to enhance local pathology correlation;

[0011] 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;

[0012] Step 4: Use a progressive parameter unfreezing mechanism to ensure the orderly learning of the model from local to global;

[0013] Step 5: Dynamically adjust sample weights based on category frequency, combining multi-level classifier loss weighted fusion to balance local discrimination and global consistency;

[0014] Step 6: Generate the final classification result through multi-level prediction probability weighted averaging and dynamic threshold adjustment, and combine it with the sigmoid calibration module to improve clinical usability.

[0015] Preferably, 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, to generate local occlusion samples to enhance the anti-interference capability of the model.

[0016] Preferably, the lightweight convolution block includes:

[0017] The first layer uses a 3×3 convolutional layer with a dilation rate of 2 to expand the receptive field to capture cross-region pathological correlations;

[0018] 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.

[0019] Preferably, the progressive parameter unfreezing mechanism includes:

[0020] In the first step, only the random geometric transformation parameters and the first network layer of the ResNet50 backbone network are unfrozen, and local texture features are optimized through the intermediate classifier;

[0021] In the second step, we unfreeze the first and second network layers of the ResNet50 backbone network and use a cosine annealing learning rate strategy to prevent deep network oscillations.

[0022] 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.

[0023] The fourth step is to unfreeze the fourth network layer and global classifier of the ResNet50 backbone network, and use a cascade pyramid structure to fuse multi-scale features.

[0024] Preferably, the weight distribution is achieved by the following formula:

[0025]

[0026] In the formula, the weight The importance of features at each stage is represented by Softmax normalization. is the weight matrix, is the eigenvector of the jth stage, is the bias term, is the total number of stages, For the The characteristic vector of the stage;

[0027] Dynamically adjust sample weights based on category frequency , the expression is:

[0028]

[0029] Combined with the weighted fusion of multi-level classifier losses, the expression is:

[0030]

[0031] By jointly optimizing the stage loss With cascade loss , the total loss function is expressed as:

[0032]

[0033] in: Representation category Frequency of occurrence in the training dataset; represents the true label of the i-th sample; Indicates the total number of samples in the training batch; Indicates the sample index; Represents the model's predicted probability for the i-th sample.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] (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 achieved, which improves the detection accuracy of small lesions (<5mm³) by 18.2%;

[0036] (2) Through the 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%;

[0037] (3) Through lightweight network architecture and adaptive computing resource allocation, the computational efficiency of traditional deep models was optimized. While maintaining 98.6% classification accuracy, the single-case inference 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

[0038] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0039] Figure 1 This is a flowchart of the workflow provided by the present invention. DETAILED DESCRIPTION

[0040] The present invention will be described in detail below with reference to specific embodiments. The following examples 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 skilled in the art, several changes and improvements can be made without departing from the scope of the present invention. These all fall within the scope of protection of the present invention.

[0041] Example

[0042] This paper proposes an intelligent brain tumor classification method based on multimodal MRI feature adaptive fusion and dynamic weight allocation network (BTCN). The process is as follows: Figure 1 , which aims to achieve high-precision classification of brain tumors (brain metastases, gliomas) through staged feature fusion and dynamic parameter optimization mechanism. In view of the limitations of traditional models between local texture capture and global structure modeling, a staged feature learning framework and a dynamic parameter optimization mechanism are used to achieve high-precision classification of brain tumors (such as brain metastases and glioblastomas). 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 calcification points at the edge of the tumor) 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:

[0043] Step 1: Multimodal MRI image mosaic enhancement and local feature learning;

[0044] This step evenly divides the input MRI image (T1WI / T2WI / FLAIR) into Small blocks are then augmented with random geometric transformations (rotation ±30°, scaling 0.8-1.2x, and horizontal / vertical flipping). The augmented image is fed into the Conv1 layer of a ResNet50, where 3×3 convolutions (64 channels) and max pooling (2×2) are used to extract primary local features, capturing subtle variations in the tumor edge (such as calcifications and hemorrhages). Finally, a synergistic mechanism combining BatchNorm and ReLU is employed to achieve efficient optimization and reduce the risk of overfitting. Specifically, BatchNorm normalizes the input data distribution layer by layer to address internal covariate shift. To avoid gradient saturation, the ReLU activation function's linearity in the positive region ensures a constant gradient of 1, avoiding the problem of traditional Sigmoid / Tanh functions where the gradient approaches zero for large inputs. For example, when capturing calcifications at the tumor edge, ReLU effectively propagates small gradient signals (such as calcifications <0.5 mm), whereas the Sigmoid function tends to lose detail 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.

[0045] Step 2: ResNet50 multi-stage feature extraction and lightweight convolutional block design;

[0046] This step constructs multi-scale pathological feature representation through multi-layer feature extraction and lightweight downsampling modules. Backbone network four-stage feature extraction:

[0047] Stage 1: 3×3 convolution (64→128 channels) + maximum pooling, output feature map , focusing on pixel-level details.

[0048] Stage 2: 3×3 convolution (128→256 channels) + maximum pooling, output , capturing the primary global structure.

[0049] Stage 3: 3×3 convolution (256→512 channels) + maximum pooling, output , modeling complex pathological patterns (such as tumor heterogeneity).

[0050] Stage 4: 3×3 convolution (512→1024 channels) + maximum pooling, output , covering the full image-level context.

[0051] After the feature maps from Stage 2 to Stage 4 are output, a lightweight convolutional block consisting of two 3×3 convolutional layers is used to enhance local features. 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. Next, 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 and necrotic areas) through sparseness. Finally, the Softmax function outputs the probability distribution of brain tumor types at each stage. As an intermediate supervisory signal to optimize network training, achieve multimodal brain tumor feature compression and improve discrimination capabilities, The calculation method is as follows:

[0052]

[0053] In the above formula, 、 is the trainable weight matrix.

[0054] Step 3: Multi-level feature fusion and global classifier design;

[0055] This step converts the output vectors of Stage3 and Stage4 in the previous step into , , , generating fused features through concatenation of fully connected layers (total dimension 1536). Next, a learnable weight matrix is ​​introduced to assign dynamic weights to features at different stages (e.g., 0.5 for Stage 4, 0.25 each for Stage 3 and Stage 2), enhancing the contribution of important features. The fused features are then fed into a stack of fully connected layers (1536→768→384), ultimately outputting a probability distribution of brain tumor types (e.g., metastasis / glioma) through Softmax, enabling collaborative decision-making based on multi-scale pathological features. The learnable matrix can be calculated using the following formula:

[0056]

[0057] In the above formula, is the weight matrix, is the bias term, weight The importance of features at each stage is represented 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.

[0058] Step 4: Development of a phased unfreezing training strategy;

[0059] This step ensures orderly learning of the model from local to global by gradually unfreezing the parameters:

[0060] Step 1: Unfreeze scope of jigsaw puzzle module and local texture feature learning of Stage 1: Only the jigsaw puzzle enhancement module (random geometric transformation parameters) and the Conv1 layer of ResNet50 (3×3 convolution + maximum pooling) are retained for training, and the remaining network layers are frozen.

[0061] Training objective: Focus on capturing the texture of local pathological markers such as calcifications and hemorrhages. By randomly rotating (±30°) and scaling (0.8x-1.2x) the jigsaw puzzle module, we generate 10%-20% locally occluded samples, forcing the model to learn interference-resistant features.

[0062] 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;

[0063] Step 2: Global structural joint optimization of Stage 1 and Stage 2;

[0064] Unfreeze scope: Add a 3×3 convolutional layer (128→256 channels) and the corresponding BatchNorm parameters to Stage 2, while keeping Stages 3 and 4 frozen.

[0065] Feature fusion: The local feature map of Stage 1 and the global feature map of Stage 2 Perform cross-stage splicing (channel dimension), achieve dimension compression through 1×1 convolution (384→256 channels), and generate fusion features .

[0066] 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;

[0067] Step 3: Fusion of complex pathological patterns;

[0068] 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.

[0069] 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 reweighted to suppress noise interference.

[0070] Gradient control: Gradient clipping (threshold ±0.1) is used to limit the amplitude of deep parameter updates to avoid destroying the learned shallow features.

[0071] Step 4: Joint optimization and classification decision-making of the entire network;

[0072] Unfreeze range: Unfreeze Stage 4 (512→1024 channels) and global classifier parameters.

[0073] Multi-scale feature fusion: Through a cascaded pyramid structure, the feature maps of Stages 1 to 4 are upsampled to a unified resolution (64×64), and channel-wise attention is used to generate the final fused features.

[0074] 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 model generalization.

[0075] Dynamic adjustment of hyperparameters: The number of training rounds is gradually increased at each step, combined with a cosine annealing learning rate strategy (initial 0.01, decay rate 0.99 per epoch) to balance learning speed and stability.

[0076] Step 5: Dynamic receptive field control and parameter sharing mechanism;

[0077] This step achieves the coordinated optimization of local and global features through mathematical formulas and gradient propagation design. , 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 undergoes 1×1 convolution, BatchNorm, and ReLU for channel compression. Asymmetric convolution groups (1×3 and 3×1 convolutions in parallel) are then used to capture horizontal and vertical spatial dependencies and output feature maps. The feature maps are then normalized using a sigmoid function to generate a spatial weight matrix. Finally, to achieve cross-stage parameter sharing, the model continuously updates all stage parameters (including the jigsaw module and ResNet50) during training. Lower-level stage gradients are backpropagated to optimize higher-level stage parameters, achieving knowledge transfer and improving robustness.

[0078] Step 6: Design of dynamic weight cross entropy loss function;

[0079] To address the class imbalance problem, 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:

[0080]

[0081] in: Representation category Frequency of occurrence in the training dataset; Represents the true label (Ground Truth) of the i-th sample; Indicates 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 model's predicted probability for the i-th sample.

[0082] By jointly optimizing the stage loss With cascade loss , the total loss function can be expressed as:

[0083]

[0084] Based on weighted fusion, balance local discriminability and global consistency.

[0085] Step 7: Classification decision fusion and post-processing mechanism;

[0086] This step improves the robustness and clinical usability of classification results through weighted averaging of multi-stage predictions and probability calibration. The final classification result combines the predicted probabilities of each stage (weights are assigned based on importance, such as 0.4 for Stage 4, 0.3 for Stage 3, 0.2 for Stage 2, and 0.1 for Stage 1) with the global output, generating a final probability distribution through weighted averaging, thus achieving multi-stage prediction fusion. To reduce the risk of misdiagnosis, this module dynamically adjusts the classification threshold. Specifically, it optimizes the classification threshold based on the classification distribution (reducing the threshold to 0.3 for small sample categories). The sigmoid calibration module adjusts the output probability distribution to assist physicians in reviewing diagnostic results and enhance clinical confidence.

[0087] The above describes specific embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. The embodiments of this application and the features in the embodiments may be combined with each other in any manner unless there is a conflict.

Claims

1. A brain tumor classification method based on deep learning, characterized in that: include: Step 1: Evenly divide the input MRI image into Small blocks are generated through random geometric transformation to generate enhanced puzzles. The puzzle-enhanced image is input into the Conv1 layer of the ResNet50 backbone network, and primary local features are extracted through 3×3 convolution and maximum pooling. The input data distribution is normalized layer by layer through BatchNorm, and the gradient is kept constant at 1 through the linear characteristic of the ReLU activation function in the positive interval. Step 2: Multi-scale feature maps are extracted through the four stages of the ResNet50 backbone network, Stages 1 to 4. After the feature maps from Stages 2 to 4 are output, a lightweight convolutional block consisting of two 3×3 convolutional layers is used to enhance local features. Next, a BatchNorm layer normalizes the channels of each fully connected layer output to suppress gradient oscillation. The ReLU activation function uses sparsification to filter key pathological features. Finally, the Softmax function outputs the probability distribution of brain tumor types at each stage, which serves as an intermediate supervisory signal to optimize the training of the ResNet50 backbone network. 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 based on category frequency, combining multi-level classifier loss weighted fusion to balance local discrimination and global consistency; Step 6: Generate the final classification result through multi-level prediction probability weighted average and dynamic threshold adjustment; 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, generating local occlusion samples to enhance the model's anti-interference ability; The lightweight convolution block includes: The first layer uses a 3×3 convolutional layer with a dilation rate of 2 to expand the receptive field to capture cross-region pathological correlations; The second layer uses a depth-wise separable convolutional layer to compress the parameters and eliminates spatial redundant information through global average pooling. The progressive parameter unfreezing mechanism includes: In the first step, only the first network layer of the ResNet50 backbone network is unfrozen and the local texture features are optimized through the intermediate classifier; In the second step, we unfreeze the second network layer of the ResNet50 backbone network and use a cosine annealing learning rate strategy to prevent deep network oscillations. 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 cascade pyramid structure to fuse multi-scale features; The weight distribution is achieved through the following formula: In the formula, the weight The importance of features at each stage is represented 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: in: Representation category Frequency of occurrence in the training dataset; represents the true label of the i-th sample; Indicates the total number of samples in the training batch; Indicates the sample index; Represents the model's predicted probability for the i-th sample.

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