The goal of this research is to identify abnormality automatically in the mitral valve,stenosis, or
normal in parasternal short-axis view (PSAX).one of the common and widely spread valvular diseases is mitral
valve disease. It is still a burden in underdeveloped countries, for health sociality as well as countries. Mitral
valve approximately 80 percent of valvular diseases. According tothe World Heart Foundation Guidelines,
based on mitral leaflets morphology. The mitral valve areacan calculate using the PSAX view. In mitral stenosis
mitral valve has a specific shape, which is similar to a fish mouth. Our main goal is to detect this abnormality so
that sonographers investigate further better.Our evaluation metrics have concern f1 score for normal is 99%, and
mitral stenosis is 99%, and accuracy is 99 percent. The dataset we for training is 900 and testing 600 for testing
purposes: confusion matrix, ROC curve, PR curve measured for our evaluation result. We created the
MobileNet inspired model to solve the classification of normal or mitral stenosis valve.Our proposed model
only detectsan abnormality in the mitral valve in the PSAX view. It reduces the time of Echocardiography.We
aim to use a minimum number of parameters to solve this problem to make real-time analysis possible
CNN, deep learning, Echocardiography, mitral stenosis, PSAX, MobileNet
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