Interpretable Alzheimer’s Disease Diagnosis via CNNs and MRI: An Explainable AI Approach

Md Abubakkar

Abstract:

Alzheimer’s Disease (AD) is a progressive neurode-
generative 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.