Optimal Deep Learning based Classification Model for Mitral Valve Diagnosis System 
Anbarasi. A1, Ravi. S2
1Anbarasi. A*, Research Scholar, Department of Computer Science, Pondicherry University, Puducherry, India.
2Ravi. S, Research Scholar, Department of Computer Science, Pondicherry University, Puducherry, India.
Manuscript received on April 05, 2020. | Revised Manuscript received on April 17, 2020. | Manuscript published on April 30, 2020. | PP: 315-320 | Volume-9 Issue-4, April 2020. | Retrieval Number: C6530029320/2020©BEIESP | DOI: 10.35940/ijeat.C6530.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Mitral valve; Deep Learning; CNN; Classification; Segmentation.