Deep Learning-Based Brain Tumor MRI Image Classification System: A Medical AI Practice from CNN to Transfer Learning

This article introduces an open-source brain tumor MRI image classification project that uses CNN and transfer learning techniques to classify MRI images into four categories: glioma, meningioma, pituitary tumor, and no tumor. The article covers data preprocessing, model design, training strategies, and performance evaluation, providing a complete practical reference for medical imaging AI developers, aiming to address the issues of scarce manual image reading resources and misdiagnosis risks.

Brain tumors are common primary tumors of the central nervous system, with over 250,000 new diagnoses worldwide each year. MRI can distinguish between glioma, meningioma, pituitary tumor, and other types, but manual image reading faces two major challenges: scarcity of professional doctor resources (especially in medically underserved areas) and misdiagnosis risks due to fatigue. Developing an automated high-precision classification system has important clinical value.

The project uses a standardized MRI dataset containing four categories of images. The preprocessing workflow includes: 1. Image size standardization to unify input; 2. Pixel value normalization to the 0-1 range to accelerate convergence; 3. Data augmentation strategies such as random rotation, horizontal flipping, and scaling to simulate clinical image differences and improve model generalization ability.

1. Basic CNN Model: Uses a stacked structure of convolution-pooling-full connection. Convolutional layers extract hierarchical features (edges, textures → complex semantics), and pooling layers reduce dimensionality to enhance position invariance. Training uses cross-entropy loss, Adam optimizer, and learning rate decay. 2. Transfer Learning: To address the problem of limited medical data, pre-trained models MobileNetV2 (lightweight and efficient) and ResNet50 (residual connections solve gradient vanishing) are evaluated. By freezing the bottom layers, replacing the classifier, and fine-tuning with a small learning rate, general visual knowledge is retained while tumor-specific features are learned.

The evaluation system includes accuracy, confusion matrix, and classification report (precision/recall/F1). Results: Transfer learning models outperform the basic CNN. ResNet50 has the best classification accuracy, while MobileNetV2 has an advantage in inference speed. Error analysis found that some gliomas and meningiomas are easily confused (similar images), so it is recommended to combine multi-modal image joint analysis to improve accuracy.

The open-source implementation of the project provides a complete development reference, allowing developers to quickly build or improve the system. Future directions: Introduce attention mechanisms to focus on tumor regions; explore 3D convolution to utilize spatial structures; develop Web interfaces to lower clinical thresholds; use federated learning to implement multi-center data joint modeling to improve generalization ability.

This project demonstrates the potential of deep learning in medical image analysis. By combining the feature extraction capabilities of CNN and the data efficiency advantages of transfer learning, it provides a feasible solution for automated brain tumor diagnosis. With algorithm optimization and clinical data accumulation, AI-assisted diagnosis is expected to become a powerful assistant for radiologists, benefiting more patients.