Deep learning model for prediction of progressive mild cognitive impairment to Alzheimer's disease using structural MRI

Lim, Bing Yan and Lai, Khin Wee and Hasikin, Khairunnisa and Kulathilake, K. A. Saneera Hemantha and Ong, Zhi Chao and Hum, Yan Chai and Dhanalakshmi, Samiappan and Wu, Xiang and Zuo, Xiaowei (2022) Deep learning model for prediction of progressive mild cognitive impairment to Alzheimer's disease using structural MRI. Frontiers in Aging Neuroscience, 14. ISSN 1663-4365, DOI https://doi.org/10.3389/fnagi.2022.876202.

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Abstract

Alzheimer's disease (AD) is an irreversible neurological disorder that affects the vast majority of dementia cases, leading patients to experience gradual memory loss and cognitive function decline. Despite the lack of a cure, early detection of Alzheimer's disease permits the provision of preventive medication to slow the disease's progression. The objective of this project is to develop a computer-aided method based on a deep learning model to distinguish Alzheimer's disease (AD) from cognitively normal and its early stage, mild cognitive impairment (MCI), by just using structural MRI (sMRI). To attain this purpose, we proposed a multiclass classification method based on 3D T1-weight brain sMRI images from the ADNI database. Axial brain images were extracted from 3D MRI and fed into the convolutional neural network (CNN) for multiclass classification. Three separate models were tested: a CNN built from scratch, VGG-16, and ResNet-50. As a feature extractor, the VGG-16 and ResNet-50 convolutional bases trained on the ImageNet dataset were employed. To achieve classification, a new densely connected classifier was implemented on top of the convolutional bases.

Item Type: Article
Funders: Universiti Malaya (Grant No: PV052-2019), ACU UK (Grant No: IF063-2021)
Uncontrolled Keywords: Alzheimer's disease; Deep learning; Prediction; Magnetic resonance imaging; Mild cognitive impairment
Subjects: R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions: Faculty of Engineering > Biomedical Engineering Department
Faculty of Engineering > Department of Mechanical Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 16 Oct 2023 05:01
Last Modified: 16 Oct 2023 05:01
URI: http://eprints.um.edu.my/id/eprint/42126

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