  Alzheimer's disease (AD) is an irreversible devastative neurodegenerative
disorder associated with progressive impairment of memory and cognitive
functions. Its early diagnosis is crucial for the development of possible
future treatment option(s). Structural magnetic resonance images (sMRI) plays
an important role to help in understanding the anatomical changes related to AD
especially in its early stages. Conventional methods require the expertise of
domain experts and extract hand-picked features such as gray matter
substructures and train a classifier to distinguish AD subjects from healthy
subjects. Different from these methods, this paper proposes to construct
multiple deep 2D convolutional neural networks (2D-CNNs) to learn the various
features from local brain images which are combined to make the final
classification for AD diagnosis. The whole brain image was passed through two
transfer learning architectures; Inception version 3 and Xception; as well as
custom Convolutional Neural Network (CNN) built with the help of separable
convolutional layers which can automatically learn the generic features from
imaging data for classification. Our study is conducted using cross-sectional
T1-weighted structural MRI brain images from Open Access Series of Imaging
Studies (OASIS) database to maintain the size and contrast over different MRI
scans. Experimental results show that the transfer learning approaches exceed
the performance of non-transfer learning based approaches demonstrating the
effectiveness of these approaches for the binary AD classification task.
