Latha, S. and Muthu, P. and Dhanalakshmi, Samiappan and Kumar, R. and Lai, Khin Wee and Wu, Xiang (2022) Emerging feature extraction techniques for machine learning-based classification of carotid artery ultrasound images. Computational Intelligence and Neuroscience, 2022. ISSN 1687-5265, DOI https://doi.org/10.1155/2022/1847981.
Full text not available from this repository.Abstract
Plaque deposits in the carotid artery are the major cause of stroke and atherosclerosis. Ultrasound imaging is used as an early indicator of disease progression. Classification of the images to identify plaque presence and intima-media thickness (IMT) by machine learning algorithms requires features extracted from the images. A total of 361 images were used for feature extraction, which will assist in further classification of the carotid artery. This study presents the extraction of 65 features, which constitute of shape, texture, histogram, correlogram, and morphology features. Principal component analysis (PCA)-based feature selection is performed, and the 22 most significant features, which will improve the classification accuracy, are selected. Naive Bayes algorithm and dynamic learning vector quantization (DLVQ)-based machine learning classifications are performed with the extracted and selected features, and analysis is performed.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Malaya, ACU UK [Grant No: IF063-2021] |
| Uncontrolled Keywords: | Integrated-system; Segmentation; Thickness; Cartilage |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 16 Oct 2023 01:52 |
| Last Modified: | 16 Oct 2023 01:52 |
| URI: | http://eprints.um.edu.my/id/eprint/42199 |
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