An Attention-Based CNN Framework for Alzheimer’s Disease Staging with Multi-Technique XAI Visualization

Alzheimer’s disease (AD) is a significant challenge in modern healthcare, with early detection and accurate staging remaining critical priorities for effective intervention. While Deep Learning (DL) approaches have shown promise in AD diagnosis, existing methods often struggle with the issues of precision, interpretability, and class imbalance. This study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence (XAI) techniques, in particular attention mechanisms, Gradient-Weighted Class Activation Mapping (Grad-CAM), and Local Interpretable Model-Agnostic Explanations (LIME), to improve both model interpretability and feature selection. The study evaluates four different DL architectures (ResMLP, VGG16, Xception, and Convolutional Neural Network (CNN) with attention mechanism) on a balanced dataset of 3714 MRI brain scans from patients aged 70 and older. The proposed CNN with attention model achieved superior performance, demonstrating 99.18% accuracy on the primary dataset and 96.64% accuracy on the ADNI dataset, significantly advancing the state-of-the-art in AD classification. The ability of the framework to provide comprehensive, interpretable results through multiple visualization techniques while maintaining high classification accuracy represents a significant advancement in the computational diagnosis of AD, potentially enabling more accurate and earlier intervention in clinical settings.