Binary Classification of Alzheimer Disease using sMRI Imaging modality and Deep Learning

TL;DR

This paper introduces a deep learning approach using transfer learning and custom CNNs to classify Alzheimer's disease from structural MRI images, outperforming traditional feature-based methods.

Contribution

It proposes a novel combination of transfer learning architectures and custom CNNs for automatic AD diagnosis from sMRI images, eliminating the need for manual feature extraction.

Findings

Transfer learning approaches outperform non-transfer learning methods.

Inception v3 and Xception architectures achieve high classification accuracy.

Deep learning models automatically learn relevant features from MRI data.

Abstract

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.