Explainable Deep Learning Based Brain Tumor Classification Using ResNet50 with HiRes-CAM Visualization and Spatial Analysis

Brain tumors are among the leading causes of cancer-related mortality worldwide, making early and accurate diagnosis essential for improving patient survival and treatment outcomes. Manual interpretation of MRI scans by radiologists remains challenging due to human fatigue, inter-observer variability, and complex tumor appearances, with reported diagnostic error rates of 20 to 30 percent. This paper proposes an explainable deep learning-based brain tumor classification system using ResNet50 with a three-phase progressive fine-tuning strategy, trained on 7,210 MRI images across four classes namely Glioma, Meningioma, No Tumor, and Pituitary tumor. Three explainable AI methods namely Grad-CAM, Grad-CAM++, and HiRes-CAM were comparatively evaluated, and HiRes-CAM was selected based on its superior sharpness scores for precise tumor localization. The system additionally extracts ten clinically relevant spatial parameters including anatomical region, tumor volume, hemisphere, midline shift, and edema volume to support medical decision-making. The proposed system achieved a test accuracy of 95.00%, sensitivity of 0.950, specificity of 0.985, and F1-score of 0.9488, with AUC scores reaching 0.9997 for Pituitary tumor. The complete system is deployed as a web-based clinical application named NeuroScan AI, supporting real-time analysis at 85 to 120 milliseconds, batch processing, patient history management, and automated PDF report generation.