Explainable Deep Learning For Brain Tumor Detection using CNN And Grad-CAM

Abstract

The integration of Artificial Intelligence in medical imaging offers significant potential for enhancing diagnostic speed and accuracy, yet the “black-box” nature of deep learning models remains a barrier to clinical adoption. This paper presents a robust framework for brain tumor detection utilizing a Convolutional Neural Network (CNN) integrated with Gradient-weighted Class Activation Mapping (Grad-CAM) to provide anatomical explainability. The proposed model utilizes a three-tier convolutional architecture optimized with Adam and binary cross-entropy loss. Experimental results on a brain MRI dataset demonstrate a training accuracy of 84.47% and a validation accuracy of 82.93%, indicating strong generalization capabilities. To bridge the gap between performance and interpretability, Grad-CAM was employed to generate visual heatmaps that localize the pathological regions of interest. These visualizations confirm that the model’s classifications are grounded in relevant tissue irregularities rather than image artifacts. This study demonstrates how explainable deep learning can serve as a transparent decision-support tool, fostering trust and providing verifiable diagnostic assistance for medical professionals.