Development of Brain Tumor Classification System using Convolutional Neural Network Model with Explainable Artificial Intelligence

Abstract

Brain tumors are abnormal cell growths in the brain that require accurate and timely diagnosis, where Magnetic Resonance Imaging (MRI) plays a critical role in detecting and characterizing tumor structures. However, accurate interpretation of Magnetic Resonance Imaging (MRI) scans is challenging due to their complexity and the limited availability of expert radiologists. This challenge is further compounded by the lack of interpretability in many existing deep learning-based diagnostic systems. Therefore, the need for an automated and interpretable brain tumor classification system arises, which is the problem this study aims to address. In this research, a brain tumor classification system integrated with Explainable Artificial Intelligence (XAI) was developed using MRI images. The model was designed to classify brain tumors into glioma, meningioma, pituitary tumor, and no-tumor categories while providing visual explanations for its predictions using Grad-Class Activation Mapping (Grad-CAM). The performance of the system was evaluated for each tumor category using accuracy, precision, specificity, recall, F1-Score, false positive rate and also the Receiver Operating Characteristic and Area Under Curve (ROC-AUC). Experimental results show that the developed CNN model achieved an overall classification accuracy of 90.6% with an AUC-ROC value of 0.9892, indicating strong discriminative capability across tumor classes. The Grad-CAM visualizations consistently highlighted tumor-affected regions in the MRI images, confirming that the model based its predictions on clinically relevant anatomical structures. The developed model demonstrated effective classification performance and improved interpretability, making it suitable as a reliable decision-support tool for automated brain tumor diagnosis