Study for Aortic Atherosclerotic Plaque Classification Using Deep Learning

Stroke and heart disease, which account for a high percentage of the causes of death amongst the elderly population, can occur suddenly leading to death. Hence, early diagnoses and continuous management are required. High-risk diseases should be diagnosed through medical personnel using established medical techniques. However, it is time consuming to decide on a diagnosis or the opinion may differ depending on the medical professional. This study aims to shorten the diagnosis period and provide high accuracy diagnoses by establishing the semi-supervised convolution autoencoder and the U-Net models that can classify aortic atherosclerotic plaque conditions and predict the primary locations for stroke occurrence.