An Explainable AI-Powered Performance Weighted Ensemble Deep Learning System for Classifying MRI Brain Tumours

This work investigates the classification of brain tumors from MRI scans using a variety of deep learning architectures, such as Light U-Net, DenseNet121, Attention U-Net, and Custom Convolutional Neural Network (CNN). To increase accuracy, an ensemble-based classifier was created with a weighted soft voting technique. Explainable AI (XAI) approaches, like Gradient-weighted Class Activation Mapping (Grad-CAM), were used to improve clinical applicability by visualization. With an accuracy rate of 97.93\% in differentiating between gliomas, meningiomas, pituitary adenomas, and healthy patients, the ensemble model behaved better than single structures. Heatmaps produced by Grad-CAM showed important areas affecting model predictions, matching radiological characteristics and boosting confidence in the outcomes.