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

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)

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%.

Original languageEnglish
Article number42
Pages (from-to)1-19
Number of pages19
JournalCommunications in Mathematical Biology and Neuroscience
Volume2020
DOIs
Publication statusPublished - 2020

Keywords

  • COVID-19
  • Convolution
  • GoogleNet
  • Resnet
  • SVM

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