Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network

Dian Candra Rini Novitasari, Rimuljo Hendradi, Rezzy Eko Caraka, Yuanita Rachmawati, Nurul Zainal Fanani, Anang Syarifudin, Toni Toharudin, Rung Ching Chen

Abstract


This study aims to detect whether patients examined are healthy, Coronavirus positive, or just have pneumonia based on chest X-ray data using Convolutional Neural Network method as feature extraction and Support Vector Machine as a classification method or called Convolutional Support Vector Machine. Experiments carried out were comparing the kernel used, feature selection methods, architecture in feature extraction, and separated classes. Our instrument reached the accuracy of 97.33% in the separation of 3 classes (normal, pneumonia, COVID19) and 100% in the separation of 2 classes, that is (normal, COVID19) and (pneumonia, COVID19), respectively. Based on these results, it can be concluded that the feature selection method can improve gained accuracy ±98%.

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Published: 2020-07-14

How to Cite this Article:

Dian Candra Rini Novitasari, Rimuljo Hendradi, Rezzy Eko Caraka, Yuanita Rachmawati, Nurul Zainal Fanani, Anang Syarifudin, Toni Toharudin, Rung Ching Chen, Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network, Commun. Math. Biol. Neurosci., 2020 (2020), Article ID 42

Copyright © 2020 Dian Candra Rini Novitasari, Rimuljo Hendradi, Rezzy Eko Caraka, Yuanita Rachmawati, Nurul Zainal Fanani, Anang Syarifudin, Toni Toharudin, Rung Ching Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Commun. Math. Biol. Neurosci.

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