Latha, S. and Muthu, P. and Lai, Khin Wee and Khalil, Azira and Dhanalakshmi, Samiappan (2022) Performance analysis of machine learning and deep learning architectures on early stroke detection using carotid artery ultrasound images. Frontiers in Aging Neuroscience, 13. ISSN 1663-4365, DOI https://doi.org/10.3389/fnagi.2021.828214.
Full text not available from this repository.Abstract
Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to provide the extent of stenosis by examining the intima-media thickness and plaque diameter. A total of 361 images were classified using machine learning and deep learning approaches to recognize whether the person is symptomatic or asymptomatic. CART decision tree, random forest, and logistic regression machine learning algorithms, convolutional neural network (CNN), Mobilenet, and Capsulenet deep learning algorithms were applied in 202 normal images and 159 images with carotid plaque. Random forest provided a competitive accuracy of 91.41% and Capsulenet transfer learning approach gave 96.7% accuracy in classifying the carotid artery ultrasound image database.
| Item Type: | Article |
|---|---|
| Funders: | Institution of Engineers India [RDDR2016064] |
| Uncontrolled Keywords: | Carotid artery; Ultrasound image; Machine learning; Deep learning; Stroke |
| Subjects: | R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 03 Aug 2022 01:05 |
| Last Modified: | 03 Aug 2022 01:05 |
| URI: | http://eprints.um.edu.my/id/eprint/33456 |
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