  Alzheimer's Disease (AD) is a neurodegenerative disease that is one of the
significant causes of death in the elderly population. Many deep learning
techniques have been proposed to diagnose AD using Magnetic Resonance Imaging
(MRI) scans. Predicting AD using 2D slices extracted from 3D MRI scans is
challenging as the inter-slice information gets lost. To this end, we propose a
novel and lightweight framework termed 'Biceph-Net' for AD diagnosis using 2D
MRI scans that model both the intra-slice and inter-slice information.
Biceph-Net has been experimentally shown to perform similar to other
Spatio-temporal neural networks while being computationally more efficient.
Biceph-Net is also superior in performance compared to vanilla 2D convolutional
neural networks (CNN) for AD diagnosis using 2D MRI slices. Biceph-Net also has
an inbuilt neighbourhood-based model interpretation feature that can be
exploited to understand the classification decision taken by the network.
Biceph-Net experimentally achieves a test accuracy of 100% in the
classification of Cognitively Normal (CN) vs AD, 98.16% for Mild Cognitive
Impairment (MCI) vs AD, and 97.80% for CN vs MCI vs AD.
