Mitral Valve Leaflet Tracking in Echocardiography using Custom Yolo3

Abstract

Abstract Mitral Valve diseases still are killers in developing countries, around 80% of Cardiovascular disease patients are alone in countries who are developing. Cause of CVD is the presence of abnormalities in mitral leaflets, leaflets are key in mitral valve diseases. World Heart Federation guidelines for sonographers for disease detection of the mitral valve, the mitral leaflets’ morphology has a major role. The major drawback of these guidelines is, highly dependent on the experience of a sonographer. The main challenge is to use minimum parameters for the tracking of mitral leaflets. In this research proposed model tracked mitral leaflets to visualize more accurately, using the Yolo Mechanism with MobileNet backend of deep learning technique. It helps sonographers to identify mitral leaflets in the apical four-chamber view. We used a dataset of 40 echocardiography videos of different patients of the Apical four-chamber view(A4C). We achieved mAP of 84.4 with mitral valve leaflet detection accuracy 98% and tricuspid valve leaflet accuracy of 90% with 30 patients of 1800 image for training and 30 patients of 1800 for testing. Testing IoU 92.3%. The performance of the proposed model can be visualized by the PR curve drowned. This research will help to identify abnormalities in a mitral leaflet by visualizing them inside the boundary box.