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

Novitasari, Dian Candra Rini and Hendradi, Rimuljo and Caraka, Rezzy Eko Caraka and Rachmawati, Yuanita and Fanani, Nurul Zainal and Syarifudin, Anang and Toharudin, Toni and Chen, Rung Ching (2020) Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network. Communications in Mathematical Biology and Neuroscience, 2020. pp. 1-19. ISSN 2052-2541

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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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