We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Emerging Feature Extraction Techniques for Machine Learning-Based Classification of Carotid Artery Ultrasound Images.
- Authors
Latha, S.; Muthu, P.; Dhanalakshmi, Samiappan; Kumar, R.; Lai, Khin Wee; Wu, Xiang
- 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.
- Subjects
FEATURE extraction; ULTRASONIC imaging; EXTRACTION techniques; CAROTID intima-media thickness; FEATURE selection; MACHINE learning; CAROTID artery
- Publication
Computational Intelligence & Neuroscience, 2022, p1
- ISSN
1687-5265
- Publication type
Article
- DOI
10.1155/2022/1847981