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

Journal: Computational intelligence and neuroscience
PMID: 35602622

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.

Authors

  • S Latha
    Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India.
  • P Muthu
    Department of Biomedical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603202, India.
  • Samiappan Dhanalakshmi
    Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu 603203, India.
  • R Kumar
    Department of Otorhinolaryngology and Head and Neck Surgery, All India Institute of Medical Sciences, Room no. 4057, ENT Office, 4th floor, Teaching Block, Ansari Nagar, New Delhi, 110029 India.
  • Khin Wee Lai
    Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.
  • Xiang Wu

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