An optimized deep learning framework for brain tumor classification using magnetic resonance imaging

Abstract

Accurate and interpretable classification of brain tumors in magnetic resonance imaging (MRI) scans plays a crucial role in early diagnosis and effective treatment planning. This study introduces a deep learning (DL) framework based on a customized YOLOv5m architecture integrated with a bidirectional feature pyramid network (BiFPN) for multi-class brain tumor classification. The integration of BiFPN enhances multi-scale feature fusion, improving detection across varied tumor types, while YOLOv5m ensures real-time inference capabilities. To mitigate class imbalance, a class-weighted cross-entropy loss is adopted. The model is evaluated on multiple performance metrics, achieving a test accuracy of 88.86%, precision of 88.70%, recall of 88.20%, and F1-score of 88.25%. It also reports a mean average precision (mAP@0.5) of 94.36%, with high class-wise average precision (AP) for glioma, meningioma, pituitary, and no-tumor categories. Computational time for training (12484.81 seconds) and testing (146.82 seconds) confirms the model’s feasibility for real-time clinical deployment. To support interpretability, gradient-weighted class activation mapping (Grad-CAM) is integrated for visualizing class-discriminative regions, helping clinicians understand the model’s predictions. A gradio-based user interface is also developed, enabling intuitive interaction with the system.