Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques
- Submitting institution
-
Middlesex University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1320
- Type
- D - Journal article
- DOI
-
10.1016/j.eswa.2019.06.038
- Title of journal
- Expert Systems with Applications
- Article number
- -
- First page
- 353
- Volume
- 136
- Issue
- -
- ISSN
- 0957-4174
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2019
- URL
-
http://eprints.mdx.ac.uk/26903/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Machine based analysis and prediction techniques have been widely used for diagnosis of Alzheimer’s Disease (AD). This paper is significant because it addresses the issues of the lower accuracy of existing techniques and lack of post diagnosis monitoring systems. An assistive framework is proposed with novel deep learning-based diagnosis and monitoring AD-like diseases using magnetic resonance imaging scans and body worn inertial sensors, leading to a very encouraging 95% accuracy in classifying the activities of daily living and 82% improvement in patient vulnerability evaluation compared with well-known existing techniques.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -