Exploring 3D Transfer Learning CNN Models for Alzheimer's Disease Diagnosis from MRI Images

Abstract

Lately, deep learning has become increasingly popular in resolving issues across multiple domains, including medical image analysis. This research introduces a process based on deep convolutional neural networks to diagnose Alzheimer's disease and its various stages by utilizing magnetic resonance imaging (MRI) scans. Identifying Alzheimer's disease (AD) in elderly individuals can be quite difficult. This is because it harms the brain cells related to memory and thinking abilities, and it's hard to tell apart from normal brain patterns in scans. Detecting it needs a special way to represent features for sorting it out. Deep learning methods can acquire such representations from the MRI data. In this paper, five different transfer learning models are trained in 15 binary classifiers, each of them can classify two of Alzheimer's disease, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) classes. This method finds the best transfer learning model for classifying each binary comparison. The proposed technique results in best accuracy of 92% for the AD vs. CN classifier, 94% for the AD vs. MCI classifier, and 72% for the MCI vs. CN classifier, which shows the effectiveness of transfer learning in distinguishing the AD vs. CN and the AD vs. MCI cases. © 2023 IEEE.