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 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. Commun. Math. Biol. Neurosci., - (-). 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%.

Item Type: Article
Creators:
Creators
Email
NIDN
Novitasari, Dian Candra Rini
diancrini@uinsby.ac.id
2024118502
Hendradi, Rimuljo
-
-
Caraka, Rezzy Eko
-
UNSPECIFIED
Rachmawati, Yuanita
yuanitarhartono@gmail.com
2019088803
Fanani, Nurul Zainal
-
-
Syarifudin, Anang
-
-
Toharudin, Toni
-
-
Chen, Rung Ching
-
-
Uncontrolled Keywords: COVID-19; chest X-ray; vector machine; convolutional neural
Subjects: 11 MEDICAL AND HEALTH SCIENCES > 1101 Medical Biochemistry and Metabolomics > 110103 Medical Biochemistry - Inorganic Elements and Compounds
11 MEDICAL AND HEALTH SCIENCES > 1103 Clinical Sciences > 110302 Clinical Chemistry (diagnostics)
Divisions: Fakultas Tarbiyah dan Keguruan > Pendidikan Ilmu Pengetahuan Alam
Depositing User: Samidah Nurmayuni
Date Deposited: 24 Mar 2022 07:26
Last Modified: 24 Mar 2022 07:26
URI: http://repository.uinsby.ac.id/id/eprint/2237

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