PyTorch-based CNN Model for Brain Tumor Detection: Practice in AI-Assisted Medical Image Diagnosis

This article introduces a convolutional neural network (CNN) project developed using the PyTorch framework for automatic detection and classification of brain tumors from MRI scan images. The project demonstrates the application of deep learning technology in medical image analysis, providing technical support for clinical auxiliary diagnosis. The original author of the project is Berisaee, and it is published on GitHub with the link: https://github.com/Berisaee/Brain-Tumor-Detection-using-CNN-PyTorch.

Challenges in Medical Image Diagnosis

Traditional brain tumor diagnosis relies on doctors' experience, which is time-consuming and subject to subjective differences, especially prone to disagreements in early-stage tumors or those with blurred boundaries.

Reasons for Choosing CNN

Through local connection and weight sharing mechanisms, CNN can efficiently learn hierarchical features of images: shallow layers capture edge textures, middle layers recognize shape contours, and deep layers understand complex tumor morphology, making it suitable for fine recognition of medical images.

Advantages of PyTorch Framework

1. Dynamic computation graph facilitates debugging and iteration;
2. Rich ecosystem (e.g., torchvision) simplifies preprocessing and model building;
3. Active community provides resources;
4. Native support for CUDA-accelerated training.

Data Preprocessing

• Standardization: Unify the numerical range of MRI images to eliminate device differences;
• Data Augmentation: Expand samples through random rotation, flipping, cropping and scaling;
• Size Unification: Resize to fixed dimensions (e.g., 224×224).

Model Structure

• Convolutional layer block: Contains convolution, batch normalization, and activation functions;
• Pooling layer: Reduces dimensions while retaining key features;
• Dropout layer: Prevents overfitting;
• Fully connected layer: Maps to tumor category output.

Loss and Optimization

• Loss function: Cross-entropy loss (for multi-class tasks);
• Optimizer: Adam (adaptive learning rate);
• Learning rate scheduling: Adopt StepLR or ReduceLROnPlateau strategies.

Training Strategy

• Dataset split: 70% training /15% validation /15% test to ensure reasonable category distribution;
• Batch training: Use batch size of 16 or 32;
• Early stopping mechanism: Monitor validation loss to avoid overfitting.

Evaluation Metrics

• Accuracy: Intuitively reflects overall performance;
• Precision/Recall: More valuable for class-imbalanced tasks;
• F1 score: Combines precision and recall;
• Confusion matrix: Shows category misclassification情况;
• ROC curve and AUC: Evaluate model discrimination ability.

Clinical Value

• Improve diagnosis efficiency: Complete preliminary screening in seconds;
• Assist decision-making: Reduce missed diagnosis rate as a second opinion;
• Resource balance: Provide support for primary medical institutions;
• Teaching and training: Offer standardized case learning resources.

Limitations and Challenges

• Data quality dependency: High annotation cost;
• Generalization ability: Differences in images from different devices affect performance;
• Interpretability: Conflict between black-box model characteristics and medical needs;
• Regulatory compliance: Need to pass strict medical device certification.

1. Transfer learning: Fine-tune using ImageNet pre-trained models;
2. Attention mechanism: Introduce SENet/CBAM modules to focus on tumor regions;
3. Multimodal fusion: Combine T1/T2/FLAIR sequence MRI;
4. 3D convolution: Process 3D MRI volume data;
5. Joint segmentation and classification: Add tumor region segmentation branch.

This project demonstrates the application potential of deep learning in the field of medical imaging, providing a feasible auxiliary diagnosis solution through an end-to-end pipeline. In the future, with the improvement of computing power and accumulation of datasets, medical imaging AI will gradually move towards clinical practice. We look forward to more open-source projects to promote technological progress, and developers participating in such projects can contribute to the development of medical and health undertakings.