Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Raza, Mohsin ORCID logoORCID: https://orcid.org/0000-0002-7351-9749, Awais, M., Ellahi, W., Aslam, N. ORCID logoORCID: https://orcid.org/0000-0002-9500-3970, Nguyen, Huan X. ORCID logoORCID: https://orcid.org/0000-0002-4105-2558 and Le-Minh, H. (2019) Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques. Expert Systems with Applications, 136 . pp. 353-364. ISSN 0957-4174 [Article] (doi:10.1016/j.eswa.2019.06.038)

PDF - Final accepted version (with author's formatting)
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0.

Download (1MB) | Preview


Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer and Communications Engineering
Item ID: 26903
Notes on copyright: © 2019. This author's accepted manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Useful Links:
Depositing User: Jisc Publications Router
Date Deposited: 04 Jul 2019 07:48
Last Modified: 29 Nov 2022 18:43
URI: https://eprints.mdx.ac.uk/id/eprint/26903

Actions (login required)

View Item View Item


Activity Overview
6 month trend
6 month trend

Additional statistics are available via IRStats2.