Zhao, Zhen and Chuah, Joon Huang and Chow, Chee-Onn and Xia, Kaijian and Tee, Yee Kai and Hum, Yan Chai and Lai, Khin Wee (2024) Machine learning approaches in comparative studies for Alzheimer's diagnosis using 2D MRI slices. Turkish Journal of Electrical Engineering and Computer Sciences, 32 (1). ISSN 1300-0632, DOI https://doi.org/10.55730/1300-0632.4057.
Full text not available from this repository.Abstract
Alzheimer's disease (AD) is an illness that involves a gradual and irreversible degeneration of the brain. It is crucial to establish a precise diagnosis of AD early on in order to enable prompt therapies and prevent further deterioration. Researchers are currently focusing increasing attention on investigating the potential of machine learning techniques to simplify the automated diagnosis of AD using neuroimaging. The present study involved a comparison of models for the detection of AD through the utilization of 2D image slices obtained from magnetic resonance imaging brain scans. Five models, namely ResNet, ConvNeXt, CaiT, Swin Transformer, and CVT, were implemented to learn features and classify AD based on various perspectives of 2D image slices. A series of experiments were conducted using the dataset from the Alzheimer's Disease Neuroimaging Initiative. The results showed that ConvNeXt outperformed ResNet, CaiT, Swin Transformer, and CVT. ConvNeXt exhibited an average accuracy, precision, recall, and F1 score of 95.74%, 96.71%, 95.74%, and 96.14%, respectively, when applied to a 3-way classification task involving AD, mild cognitive impairment, and normal control subjects. The results suggest that the utilization of ConvNeXt may have potential in the identification of AD using 2D slice images.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Malaya (MG007-2023) |
| Uncontrolled Keywords: | Alzheimer's disease; convolutional neural network; transformer; classification; magnetic resonance imaging |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 14 Nov 2024 03:31 |
| Last Modified: | 14 Nov 2024 03:31 |
| URI: | http://eprints.um.edu.my/id/eprint/45913 |
Actions (login required)
 |
View Item |
