Multi-Class MRI Brain Tumor Classification Using a Hybrid EfficientNet-Attention Architecture

The significant contribution of this research is the development of EMA-BrainNet, a lightweight hybrid deep learning framework that integrates an EfficientNet-B0 convolutional backbone with a Multi-Head Attention (MHA) mechanism for accurate multi-class brain tumor classification. Unlike conventional CNN-based approaches that primarily focus on local features, the proposed framework simultaneously captures detailed anatomical patterns and long-range spatial dependencies, enabling more effective discrimination among glioma, meningioma, pituitary tumor, and healthy brain MRI images. The study further introduces a systematic preprocessing pipeline involving CLAHE, standardized image resizing, Z-score normalization, and class-preserving data augmentation to improve feature quality and model generalization. Experimental evaluation on a balanced dataset of 8,000 MRI images demonstrates that EMA-BrainNet achieves 99.97% training accuracy, 98.88% validation accuracy, and 99.38% test accuracy, outperforming the evaluated baseline models and recent competing approaches. In addition, the integration of Grad-CAM provides visual explanations of the model’s decisions by highlighting clinically relevant tumor regions, improving the interpretability of the automated diagnostic system. Overall, the research contributes an efficient, accurate, and interpretable framework with strong potential for computer-aided brain tumor diagnosis.