Interpretable Alzheimer's Disease Diagnosis Via CNNs and MRI: an Explainable AI Approach

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that impairs memory, cognition, and daily functioning, often accompanied by significant behavioral and personality changes in older adults. While there is currently no cure, treatment interventions are most effective during the early and middle stages, emphasizing the critical need for early diagnosis—especially as global aging trends contribute to a rising prevalence of AD. This study aims to enhance early detection of Alzheimer's Disease by leveraging machine learning and deep learning techniques on MRI scans from the Open Access Series of Imaging Studies (OASIS) dataset. We employed a diverse set of models, including Random Forest and Logistic Regression from classical machine learning, Extra Trees from ensemble learning, and Convolutional Neural Networks (CNNs) from deep learning. Performance was assessed using accuracy, precision, recall, and area under the curve (AUC). Results indicate that CNNs achieved the highest accuracy and AUC scores, while Extra Trees excelled in precision and recall—highlighting the potential of both deep learning and ensemble-based methods in supporting early AD diagnosis through neuroimaging.