Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

COVID-19 is a disease or virus that has recently spread worldwide. The disease has also taken many casualties because the virus is notoriously deadly. An examination can be carried out using a chest X-Ray because it costs cheaper compared to swab and PCR tests. The data used in this study was chest...

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Autores principales:	Vivin Umrotul M. Maksum, Dian C. Rini Novitasari, Abdulloh Hamid
Formato:	article
Lenguaje:	EN
Publicado:	Department of Mathematics, UIN Sunan Ampel Surabaya 2021
Materias:	covid-19 x-ray image cad glcm elm Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/2f5f55a2ec74490b88b4b2d13814088b
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spelling	oai:doaj.org-article:2f5f55a2ec74490b88b4b2d13814088b2021-12-02T18:47:43ZImage X-Ray Classification for COVID-19 Detection Using GCLM-ELM2527-31592527-316710.15642/mantik.2021.7.1.74-85https://doaj.org/article/2f5f55a2ec74490b88b4b2d13814088b2021-05-01T00:00:00Zhttp://jurnalsaintek.uinsby.ac.id/index.php/mantik/article/view/1173https://doaj.org/toc/2527-3159https://doaj.org/toc/2527-3167COVID-19 is a disease or virus that has recently spread worldwide. The disease has also taken many casualties because the virus is notoriously deadly. An examination can be carried out using a chest X-Ray because it costs cheaper compared to swab and PCR tests. The data used in this study was chest X-Ray image data. Chest X-Ray images can be identified using Computer-Aided Diagnosis by utilizing machine learning classification. The first step was the preprocessing stage and feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). The result of the feature extraction was then used at the classification stage. The classification process used was Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) is one of the artificial neural networks with advanced feedforward which has one hidden layer called Single Hidden Layer Feedforward Neural Networks (SLFNs). The results obtained by GLCM feature extraction and classification using ELM achieved the best accuracy of 91.21%, the sensitivity of 100%, and the specificity of 91% at 135° rotation using linear activation function with 15 hidden nodes.Vivin Umrotul M. MaksumDian C. Rini NovitasariAbdulloh HamidDepartment of Mathematics, UIN Sunan Ampel Surabayaarticlecovid-19x-ray imagecadglcmelmMathematicsQA1-939ENMantik: Jurnal Matematika, Vol 7, Iss 1, Pp 74-85 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	covid-19 x-ray image cad glcm elm Mathematics QA1-939
spellingShingle	covid-19 x-ray image cad glcm elm Mathematics QA1-939 Vivin Umrotul M. Maksum Dian C. Rini Novitasari Abdulloh Hamid Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM
description	COVID-19 is a disease or virus that has recently spread worldwide. The disease has also taken many casualties because the virus is notoriously deadly. An examination can be carried out using a chest X-Ray because it costs cheaper compared to swab and PCR tests. The data used in this study was chest X-Ray image data. Chest X-Ray images can be identified using Computer-Aided Diagnosis by utilizing machine learning classification. The first step was the preprocessing stage and feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). The result of the feature extraction was then used at the classification stage. The classification process used was Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) is one of the artificial neural networks with advanced feedforward which has one hidden layer called Single Hidden Layer Feedforward Neural Networks (SLFNs). The results obtained by GLCM feature extraction and classification using ELM achieved the best accuracy of 91.21%, the sensitivity of 100%, and the specificity of 91% at 135° rotation using linear activation function with 15 hidden nodes.
format	article
author	Vivin Umrotul M. Maksum Dian C. Rini Novitasari Abdulloh Hamid
author_facet	Vivin Umrotul M. Maksum Dian C. Rini Novitasari Abdulloh Hamid
author_sort	Vivin Umrotul M. Maksum
title	Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM
title_short	Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM
title_full	Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM
title_fullStr	Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM
title_full_unstemmed	Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM
title_sort	image x-ray classification for covid-19 detection using gclm-elm
publisher	Department of Mathematics, UIN Sunan Ampel Surabaya
publishDate	2021
url	https://doaj.org/article/2f5f55a2ec74490b88b4b2d13814088b
work_keys_str_mv	AT vivinumrotulmmaksum imagexrayclassificationforcovid19detectionusinggclmelm AT diancrininovitasari imagexrayclassificationforcovid19detectionusinggclmelm AT abdullohhamid imagexrayclassificationforcovid19detectionusinggclmelm
_version_	1718377614008123392

Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

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