Classification of Plaque in Carotid Artery Using Intravascular Ultrasound Images (IVUS) by Machine Learning Techniques

Abstract

The detection of plaque in carotid artery is essential to prevent stroke, cardiovascular diseases and long-term disabilities. Stroke is considered as the major cause of death these years. In this work, Intra Vascular Ultrasound (IVUS) images of carotid artery are used for classifying the plaque deposited images from the normal one. The features that are extracted from the IVUS images includes: Mean, Standard Deviation, Energy, Histogram, Entropy and Contrast. These are fed as the input to the machine learning architecture. The machine learning algorithms employed in this study are K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision Tree and Bagged Trees. Among the four classifiers used, bagged trees showed higher accuracy of 96.2%, sensitivity of 92.6% and specificity of 96.8%.