Multi-classification machine learning for diagnosing COVID-19 in X-ray

dc.contributor.authorMayada Jabbar Kelain
dc.date.accessioned2026-01-02T12:08:33Z
dc.date.issued2023-02-13
dc.description.abstractThe dangerous COVID-19 virus is a threat to all human beings around the world. Effective identification of COVID-19 using advanced machine learning methods is a timely need. Although many complex methods have been proposed in the recent past, they still struggle to achieve the expected performance in classifying and identifying COVID-19 patients using chest X-rays. In addition, most of them are involved in the complex pretreatment task, which is often difficult for a virologist. Meanwhile, deep networks are comprehensive and have shown promising results in image recognition tasks over the past decade. In this work, chest x-ray images were used after processing the images using filters, as well as determining the infection with the virus and its classification by the SVM algorithm, as the algorithm gave good and effective results in knowing the person infected with corona or not
dc.formatapplication/pdf
dc.identifier.urihttps://geniusjournals.org/index.php/ejet/article/view/3340
dc.identifier.urihttps://asianeducationindex.com/handle/123456789/78645
dc.language.isoeng
dc.publisherGenius Journals
dc.relationhttps://geniusjournals.org/index.php/ejet/article/view/3340/2837
dc.rightshttps://creativecommons.org/licenses/by-nc/4.0
dc.sourceEurasian Journal of Engineering and Technology; Vol. 15 (2023): EJET; 13-16
dc.source2795-7640
dc.subjectexpected
dc.subjectalgorithm
dc.subjecteffective
dc.subjectinfected
dc.subjectcorona
dc.titleMulti-classification machine learning for diagnosing COVID-19 in X-ray
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typePeer-reviewed Article

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