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Computer Aided Decision Support System for Mitral Valve Diagnosis and Classification Using Depthwise Separable Convolution Neural Network by A. Anbarasi; S. Ravi; J. Vaishnavi; S. V. Suresh Babu Matla is a Computer Science article available to read on EtoBox.

What is Computer Aided Decision Support System for Mitral Valve Diagnosis and Classification Using Depthwise Separable Convolution Neural Network about?

The significance of mitral valve (MV) treatment is increasing recently because of an aging population. The computer vision-based acquisition and quantification of the valve anatomy becomes helpful for surgical and intercessional planning. The right option of common treatment and implantation is pertinent for the most favorable results. Several studies reported that the decision support system (DSS) could offer decisions based on the virtual involvement planning and prediction models. Generally, the segmentation and classification of MV from the computed tomography (CT) images are highly complicated, owing to the variations in appearance and visibility. In this paper, an efficient automated DSS model is introduced using watershed segmentation with Xception model for the MV classification. It incorporates four modules: bilateral filtering (BF) based preprocessing, watershed segmentation, Xception based feature extraction and random forest (RF) classification. A watershed algorithm with channel separation is used to segment the MV images. The Xception model with random forest (RF) model is utilized for training and classifying images. A detailed simulation is performed on the CT image

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Author

A. Anbarasi; S. Ravi; J. Vaishnavi; S. V. Suresh Babu Matla

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)