Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

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dc.contributor.authorAlquran, Hiam
dc.contributor.authorAlsleti, Mohammad
dc.contributor.authorAlsharif, Roaa
dc.contributor.authorAbu Qasmieh, Isam
dc.contributor.authorAlqudah, Ali Mohammad
dc.contributor.authorBinti Harun, Nor Hazlyna
dc.coverage.issue1cs
dc.coverage.volume27cs
dc.date.accessioned2021-08-10T12:39:18Z
dc.date.available2021-08-10T12:39:18Z
dc.date.issued2021-06-21cs
dc.description.abstractThe novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient; as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival.en
dc.formattextcs
dc.format.extent7-14cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2021 vol. 27, č. 1, s. 7-14. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2021.1.009en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttp://hdl.handle.net/11012/200935
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/128cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectCOVID-19en
dc.subjectCOVID-19 pandemicen
dc.subjectRespiratory infection detectionen
dc.subjectPneumoniaen
dc.subjectK-Nearest Neighboren
dc.subjectSupport Vector Machineen
dc.subjectRandom Foresten
dc.titleEmploying Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classificationen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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