A Multi-Modal Attention-Guided Network for Alzheimer's Disease Classification Using Deep Learning

Alzheimer's Disease (AD) is a chronic neurodegenerative disease that affects a large portion of the global population, and early and accurate diagnosis is the key component to proper management and treatment. This work presents the MAGNet model, a novel Deep Learning (DL) architecture for AD classification based on multi-modal imaging data. The MAGNet model uses multi-modal attention that is capable of hierarchically fusing structural MRI, functional MRI, and PET scans to obtain different kinds of information. The MAGNet model was tested on three datasets, with an overall accuracy of 96.2% when distinguishing between the AD, MCI, and CN groups. The proposed approach surpasses benchmark models by achieving 3.5% better accuracy and 5.2% higher sensitivity for early MCI diagnosis. Moreover, the MAGNet model offers interpretable results, employing attention visualization to support clinicians' decisions. MAGNet has the potential to predict cognitive scores and brain age with MMSE errors of 1.8 and a brain age of 2.3 years.