Carotid atherosclerotic plaque deposition leads to arterial stenosis and severe catastrophic events over time (stroke and Transitional Ischemic Attack (TIA)). It is well established that the tissue composition plays a central role for the stability and vulnerability of atherosclerotic plaques. Identification of atherosclerotic plaque components is essential to pre-estimate the risk of cardiovascular disease and stratify them as a high/low risk. The main aim of this study was to identify the plaque types such as lipid, fibrous and calcified tissue, by applying the deep learning methods on US images.