Emerging Feature Extraction Techniques for Machine Learning-Based Classification of Carotid Artery Ultrasound Images

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.PMID:35602622 | PMC:PMC9119795 | DOI:10.1155/2022/1847981

Source: Atherosclerosis - May 23, 2022 Category: Cardiology Authors: S Latha P Muthu Samiappan Dhanalakshmi R Kumar Khin Wee Lai Xiang Wu Source Type: research