Blood Vessels Disease Detection of Coronary Angiography Images using Deep learning Model

Abstract Presently Coronary artery disease, often caused by the narrowing of the coronary artery lumen due to atherosclerosis, is a leading cause of death. Coronary angiography also known as cardiac catheterization or X-ray angiography, is a medical procedure that uses X-ray imaging to visualize the coronary arteries, which supply blood to the heart muscle. X-ray angiography is procedure to assess the blood flow through these arteries and to identify any blockages or abnormalities. The accuracy of X-ray angiography depends on the quality of the imaging equipment as well as experience and expertise of the radiologist. Poor image quality could affect the accurate diagnosis of coronary arteries. Manual interpretation of angiography images is subjective and time consuming. In some cases, small or diffuse blockages may not be easily visible, and additional imaging techniques may be required. Therefore, early automated detection of blockage of heart vessels became necessary for detection and diagnosis. The artificial intelligence algorithms could play a vital role in this area. In this paper, a deep-learning based algorithm has been used for recognition of blockage in coronary angiographic visuals. Here, we proposed deep learning (YOLOv8) models for the detection of blockage into blood vessels coronary angiography images. In this experiment about 1934 labelled X-ray angiography images has been used from Mendeley. For Experimentation purpose, images are preprocessed and augmented. Total 80% images have been used for training and 20% images has been used for testing. The experimental results show that the measuring metrices of proposed model for detection of blood vessels blockage area in rectangular box. The performance of model represented by predicted value of Precision, recall, mean average precision (mAP) and F1 score are, 99.4%, 100%, 99.5% and 99.7% respectively.