The Atheromatic computer-aided diagnosis system using a support vector machine classifier achieved 83.7% accuracy in classifying carotid ultrasound plaques as symptomatic or asymptomatic.
Does the Atheromatic CAD system accurately classify carotid plaques as symptomatic or asymptomatic in ultrasound images?
The Atheromatic CAD system demonstrated 83.7% accuracy in classifying carotid ultrasound plaques as symptomatic or asymptomatic, suggesting potential utility in automated atherosclerosis assessment.
Computer-aided diagnosis (CAD) of carotid atherosclerosis into symptomatic or asymptomatic is useful in the analysis of cardiac health. This paper describes a patented CAD system called Atheromatic™ for symptomatic versus asymptomatic plaque classification in carotid ultrasound images. The system involves two steps: 1) feature extraction using a combination of discrete wavelet transform and averaging algorithms and 2) classification using a support vector machine (SVM) classifier for automated decision making. The CAD system was evaluated using a database consisting of 150 asymptomatic and 196 symptomatic plaque regions which were labeled using the ground truth based on the presence or absence of symptoms. Threefold cross-validation protocol was adapted for developing and testing the classifiers. We observed that the SVM classifier with a polynomial kernel of order 2 was to achieve a classification accuracy of 83.7%.
Acharya et al. (Tue,) conducted a other in Carotid atherosclerosis (n=346). Atheromatic CAD system vs. Clinical ground truth was evaluated on Classification accuracy. The Atheromatic computer-aided diagnosis system using a support vector machine classifier achieved 83.7% accuracy in classifying carotid ultrasound plaques as symptomatic or asymptomatic.