Open Access

ARTICLE

Early Diagnosis of Alzheimer’s Disease Based on Convolutional Neural Networks

by Atif Mehmood^1,*, Ahed Abugabah¹, Ahmed Ali AlZubi², Louis Sanzogni³

1 College of Technological Innovation, Zayed University, Abu Dhabi Campus, FF2-0-056, UAE
2 Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia
3 Nathan Campus, Griffith University, Brisbane, Australia

* Corresponding Author: Atif Mehmood. Email:

Computer Systems Science and Engineering 2022, 43(1), 305-315. https://doi.org/10.32604/csse.2022.018520

Received 11 March 2021; Accepted 29 September 2021; Issue published 23 March 2022

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disorder, causing the most common dementia in the elderly peoples. The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA. Magnetic resonance imaging (MRI) is the leading modality used for the diagnosis of AD. Deep learning based approaches have produced impressive results in this domain. The early diagnosis of AD depends on the efficient use of classification approach. To address this issue, this study proposes a system using two convolutional neural networks (CNN) based approaches for an early diagnosis of AD automatically. In the proposed system, we use segmented MRI scans. Input data samples of three classes include 110 normal control (NC), 110 mild cognitive impairment (MCI) and 105 AD subjects are used in this paper. The data is acquired from the ADNI database and gray matter (GM) images are obtained after the segmentation of MRI subjects which are used for the classification in the proposed models. The proposed approaches segregate among NC, MCI, and AD. While testing both methods applied on the segmented data samples, the highest performance results of the classification in terms of accuracy on NC vs. AD are 95.33% and 89.87%, respectively. The proposed methods distinguish between NC vs. MCI and MCI vs. AD patients with a classification accuracy of 90.74% and 86.69%. The experimental outcomes prove that both CNN-based frameworks produced state-of-the-art accurate results for testing.

---

Keywords

---