A Trustworthy Clinical Decision Support Framework for Brain Tumor MRI Classification: Integrating Attention-Guided Learning, Explainable AI, and Out-of-Distribution Validation

Deep learning models for brain tumor diagnosis need to do more than achieve high classification accuracy. For practical use, they should also provide understandable predictions, recognize unsuitable inputs, and work within a usable clinical system. This paper presents a framework for classifying brain MRI into four categories: glioma, meningioma, pituitary tumor, and no tumor. The model uses InceptionResNetV2 with a Convolutional Block Attention Module (CBAM), while Grad-CAM provides visual explanations of its predictions. A two-stage out-of-distribution (OOD) mechanism screens unsuitable inputs using an initial grayscale check followed by Mahalanobis distance, softmax confidence, and predictive entropy. These components are brought together in NeuroScan AI, a real-time web application. Evaluated on a Mendeley brain tumor MRI dataset of 12,064 images, the framework achieved 98.70% validation accuracy, 96.73% test accuracy, and a macro-average AUC of 0.9965. The OOD pipeline achieved 99% detection accuracy on a preliminary 100-image benchmark. Grad-CAM visualizations showed qualitative correspondence with clinically established tumor locations across all four classes. Overall, the work brings classification, explainability, input validation, and deployment into one framework, while recognizing that broader clinical and OOD validation is still required.