An efficient techniques used for diagnosis of mitral valve regurgitation severity

Abstract

Mitral valve diseases are more common nowadays and might not show up any symptoms. The earlier diagnosis of mitral valve abnormalities such as mitral valve stenosis, mitral valve prolapses and mitral valve regurgitation is most important in order to avoid complex situation. Many existing methodologies such as heart sound investigation model, 3-layered artificial neural network (ANN) of phonocardiogram recordings, 3-layer artificial neural network (ANN) of phonocardiogram recording, echocardiography techniques and so on are there for Mitral Valve diagnosis, but still most of the methods suffer from inefficient image segmentation and misclassification problems. In order to address this issue, this paper proposes two techniques namely 1) Deep Learning based Convolutional Neural Network (CNN) model for Mitral Valve classification model meant for diagnosis and edge detection-based segmentation model to enhance the classifier accuracy. 2) Watershed Segmentation for Mitral Valve identification and image segmentation and Xception model with Random Forest (RF) classifier for training and classification. The proposed models are evaluated in terms of three parameters namely accuracy, sensitivity and specificity, which proved that the proposed models are efficient and appropriate for Mitral Valve diagnosis.