Abstract
In present days, the domain of mitral valve (MV) diagnosis so common due to the changing lifestyle in day to day life. The increased number of MV disease necessitates the development of automated disease diagnosis model based on segmentation and classification. This paper makes use of deep learning (DL) model to develop a MV classification model to diagnose the severity level. For the accurate classification of ML, this paper applies the DL model called convolution neural network (CNN-MV) model. And, an edge detection based segmentation model is also applied which will helps to further enhance the performance of the classifier. Due to the non-availability of MV dataset, we have collected a MV dataset of our own from a total of 211 instances. A set of three validation parameters namely accuracy, sensitivity and specificity are applied to indicate the effective operation of the CNN-MV model. The obtained simulation outcome pointed out that the presented CNN-MV model functions as an appropriate tool for MV diagnosis.
Highlights
In every human body, heart is an important part and the heart related disease is considered as a major reason for increased death rate around the globe
Different works has been done to diagnose the mitral valve (MV) problem, there is still a need to properly accomplish in various ways.This paper makes use of deep learning (DL) model to develop a MV classification model to diagnose the severity level
A set of three validation attributes like accuracy, sensitivity and specificity are applied to indicate the effective operation of the CNN-MV model
Summary
Heart is an important part and the heart related disease is considered as a major reason for increased death rate around the globe. Defined a smart PDA based applicable digital phonocardiograph which cannot record the heart beats; but, different signal processing as well as statistical models are applied to classify the signals as 4 sets like S1, systole, S2 and diastole. It stimulates the Multilayer Perceptron (MLP) to divide the input by using five divisions such as normal, aortic regurgitation, aortic stenosis, mitral regurgitation and mitral stenosis. A Decision Tree (DT) classification model has been applied to discover different aortic stenosis from mitral regurgitation applying heart sound Pavlopoulos et al [10].
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