Multidimensional Echocardiography Image Segmentation Using Deep Learning Convolutional Neural Network

DOI

10.2991/aer.k.211129.069How to use a DOI?

Keywords

echocardiography; deep learning; segmentation; convolutional neural network; multidimensional

Abstract

One of the most dangerous diseases that threaten human life is heart disease. One way to analyze heart disease is by doing echocardiography. Echocardiographic test results can indicate whether the patient’s heart is normal or not by identifying the area of the heart cavity. Therefore, many studies have emerged to analyze the heart. Therefore, I am motivated to develop a system by inputting four points of view of the heart, namely 2 parasternal views (long axis and short axis) and 2 apical views (two chambers and four chambers) with the aim of this study being able to segment the heart cavity area. This research is part of a large project that aims to analyze the condition of the heart with 4 input points of view of the heart and the project is divided into several sections. For this research, it focuses on the process of echocardiographic image segmentation to obtain images of the heart cavity with 4 input points of view of the heart using the Deep Learning method by using the VGG-16 and RESNET-18 architecture. The training process is done using 30 epochs with 50 iterations per epoch and 1 batch size so that the total iteration is 7500 iterations. It can be seen that during the training process, the percentage accuracy is already high, reaching 95% -99%. On the VGG-16 architecture, it has an average accuracy in each viewpoint of around 83% -93%. The architecture of RESNET-18 has an average accuracy in every point of view which is around 76% -92%.

Copyright

© 2021 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Hasan Imaduddin
AU  - Riyanto Sigit
AU  - Anhar Risnumawan
PY  - 2021
DA  - 2021/11/30
TI  - Multidimensional Echocardiography Image Segmentation Using Deep Learning Convolutional Neural Network
BT  - Proceedings of the International Conference on Innovation in Science and Technology (ICIST 2020)
PB  - Atlantis Press
SP  - 326
EP  - 330
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.211129.069
DO  - 10.2991/aer.k.211129.069
ID  - Imaduddin2021
ER  -