EXPLAINABLE BRAIN TUMOR CLASSIFICATION USING GRAD-CAM: INTERPRETING DEEP LEARNING DECISIONS IN MRI IMAGES

Abstract

Deep neural networks can classify brain MRI images with high predictive capability, yet their internal decision processes are difficult to interpret. This limitation is especially important in medical imaging, where a prediction should be supported by evidence that can be examined by clinicians and researchers. This paper presents an explainable artificial intelligence framework for brain tumor classification using Gradient-weighted Class Activation Mapping (Grad-CAM). The proposed framework generates class-specific heatmaps from a trained convolutional network and overlays them on MRI images to show the regions that most strongly influence a prediction. The paper explains the mathematical intuition of Grad-CAM, provides a step-by-step implementation and validation protocol, and distinguishes visually attractive heatmaps from clinically meaningful explanations. Particular attention is given to layer selection, normalization, faithfulness, localization quality, uncertainty, and expert review. The framework also identifies common failure modes, including attention to text markers, skull boundaries, background artifacts, or preprocessing traces. A structured reporting checklist is proposed so that explanation results are assessed alongside classification performance. The paper concludes that Grad-CAM can improve transparency and error analysis, but it should be treated as supporting evidence rather than proof of causal reasoning or clinical correctness.