An Explainable Clinical Decision-Support Framework for Brain Tumor Detection and Classification from MRI

Abstract

Brain tumors are among the most life-threatening neurological disorders, and early, accurate diagnosis is critical for improving treatment outcomes and patient survival. MRI is the gold-standard modality for brain tumor visualization. However, proper MRI interpretation requires specialized radiological expertise, which may be limited in resource-constrained healthcare settings such as Pakistan. Furthermore, traditional deep learning methods have relied on ImageNet pre-trained models, which may not capture MRI-specific representations and usually lack a clinic-deployable decision-support system. To address these challenges, this study introduces an Explainable Clinical Decision-Support Framework for automatic brain tumor detection and four-class classification of glioma, meningioma, pituitary tumor, and no-tumor from MRI. Gradient-weighted Class Activation Mapping (Grad-CAM) generates per-prediction heatmap overlays that highlight the area contributing to each classification decision, enhancing interpretability. A two-stage learning-rate refinement strategy was employed to improve convergence and classification performance. The framework was trained and evaluated using a stratified 70%/15%/15 % split for the training, validation, and test sets, respectively, and achieved 94.79% accuracy. Comparative performance analysis against a MobileNetV2 baseline shows that the proposed customized CNN model performs better in terms of accuracy for the two difficult-to-diagnose classes of glioma and meningioma. Results indicate that the developed framework has the potential to give an accurate and interpretable classification of brain tumors from MRI imaging. Nevertheless, external validation on diverse multi-center and multi-national datasets, along with prospective clinical studies, is needed.