NeuroXAI++: An Efficient X-AI Intensive Brain Cancer Detection and Localization

Brain cancer detection and localization based on Explainable Artificial Intelligence (X-AI) technology have the potential to be a valuable tool for the accurate and efficient diagnosis of brain cancer. It can play a vital role in early diagnosis and treatment by radiologists. However, due to inaccurate AI-based diagnosis, medical experts are not incorporating it effectively into the brain cancer diagnosis process. This study proposed a robust brain cancer classification and localization method using an advanced lightweight Convolutional Neural Network (CNN) with Gradient-weighted Class Activation Mapping (Grad-CAM) visualization techniques using Magnetic Resonance Imaging (MRI) slices to fix the accuracy issues in the existing systems. Additionally, the X-AI system provides insight into the tumor's size and progression, which allows clinicians to design personalized treatment plans. Experimental result shows an accurate detection and localization of brain cancers by analyzing MRI images. It uses deep learning and computer vision approaches to identify abnormal tissue patterns associated with cancers. The proposed model obtained a higher accuracy, precision, recall, and f1-score of 99.71%, 99.53%, 99.60%, and 99.57% compared to the existing baseline models.