Deep Learning Approach to Identify Coronary Heart Disease in Its Early Stages

One of the causes of coronary artery disease is atherosclerosis. Having plaque form within your arteries is called atherosclerosis. In time, plaque builds up and narrows the interior of the conduits, which might prompt a fractional or complete blockage of blood flow. Three hundred sixty-nine thousand individuals will lose their lives to coronary heart disease this year. Of people aged 20 and over, around 18.2 million (or 6.7% of the population) have CAD. Approximately half of all coronary artery disease (CAD) fatalities occur in persons less than 65 years old. In order to aid doctors in making observation-based decisions and to diagnose diseases, image processing finds extensive usage in the medical subject. The use of cardiovascular magnetic resonance imaging (MRI) has expanded beyond its original specialty to become an integral part of cardiovascular decision-making. Cardiovascular magnetic resonance imaging (MRI) currently provides many alternatives for the detection of ischemia in individuals with known or suspected coronary artery disease (CAD). Recent years have seen a proliferation of data supporting the use of cardiovascular magnetic resonance imaging (MRI) for the diagnosis of coronary artery disease (CAD) and heart failure. For the purpose of processing MRI images of the coronary artery taken from the back plane, we provide an image processing workflow. Image segmentation is used to draw attention to the anomaly in the coronary artery. Individuals with coronary artery disease and those who are healthy are marked differently.