Computer Aided Coronary Atherosclerosis Plaque Detection and Classification

Abstract

Coronary artery disease (CAD) remains a major reason for increased mortality over the globe, comprising myocardial infarction and ischemic cardiomyopathy. The CAD is highly linked to coronary stenosis owing to the encumbrance of atherosclerotic plaques. Particularly, diversified atherosclerotic plaques are highly responsible for major cardiac adverse events over the calcified and non-calcified plaques. There, the recognition and classification of atherosclerotic plaques play a vital role to prevent and intervene in CAD. The process of detecting various class labels of the atherosclerotic plaques is significant to identify the disease at the earlier stages. Since several automated coronary plaque recognition models are mainly based on handcrafted features, it is needed to design deep learning (DL) models for improved performance. With this motivation, this study introduces an automated invasive weed optimization with densely connected networks (AIWO-DN) for coronary atherosclerosis plaque recognition and classification. Primarily, the Two Dimensional (2D) transverse cross-sectional image with the provided centreline from the input 3-D Computed Tomography Angiography (CTA) image is extracted in three orthographic aspects. In addition, the coronary lumen is segmented on every cross section and extracts the region of interest (RoI). Moreover, the Densely Connected Networks (DenseNet169) model is applied to derive the useful set of features vectors. Furthermore, invasive weed optimization (IWO) with weighted extreme learning machine (WELM) based classification model is employed to detect and classify different classes of atherosclerotic plaques. In order to validate the performance of the superior outcomes of the Automated Invasive Weed Optimization -Deep Learning (AIWO-DN) technique, a set of simulations were performed and the outcomes are inspected interms of varying metrics. The experimental results showed the betterment of the AIWO-DN technique over the existing techniques interms of several evaluation metrics.