  Acute Coronary Syndrome (ACS) is a syndrome caused by a decrease in blood
flow in the coronary arteries. The ACS is usually related to coronary
thrombosis and is primarily caused by plaque rupture followed by plaque erosion
and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most
similar lesion morphologically to a plaque rupture. In this paper, we propose
methods to classify TCFA using various machine learning classifiers including
Feed-forward Neural Network (FNN), K-Nearest Neighbor (KNN), Random Forest (RF)
and Convolutional Neural Network (CNN) to figure out a classifier that shows
optimal TCFA classification accuracy. In addition, we suggest pixel range based
feature extraction method to extract the ratio of pixels in the different
region of interests to reflect the physician's TCFA discrimination criteria. A
total of 12,325 IVUS images were labeled with corresponding OCT images to train
and evaluate the classifiers. We achieved 0.884, 0.890, 0.878 and 0.933 Area
Under the ROC Curve (AUC) in the order of using FNN, KNN, RF and CNN
classifier. As a result, the CNN classifier performed best and the top 10
features of the feature-based classifiers (FNN, KNN, RF) were found to be
similar to the physician's TCFA diagnostic criteria.
