COVID-19 detection with severity level analysis using the deep features, and wrapper-based selection of ranked features

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dc.authorscopusid56780068800
dc.authorscopusid35617556600
dc.authorscopusid55666247200
dc.contributor.authorÖksüz, Coşku
dc.contributor.authorUrhan, Oğuzhan
dc.contributor.authorGüllü, Mehmet Kemal
dc.date.accessioned2022-02-15T16:58:13Z
dc.date.available2022-02-15T16:58:13Z
dc.date.issued2021
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractThe SARS-COV-2 virus, which causes COVID-19 disease, continues to threaten the whole world with its mutations. Many methods developed for COVID-19 detection are validated on the data sets generally including severe forms of the disease. Since the severe forms of the disease have prominent signatures on X-ray images, the performance to be achieved is high. To slow the spread of the disease, effective computer-assisted screening tools with the ability to detect the mild and the moderate forms of the disease that do not have prominent signatures are needed. In this work, various pretrained networks, namely GoogLeNet, ResNet18, SqueezeNet, ShuffleNet, EfficientNetB0, and Xception, are used as feature extractors for the COVID-19 detection with severity level analysis. The best feature extraction layer for each pre-trained network is determined to optimize the performance. After that, features obtained by the best layer are selected by following a wrapper-based feature selection strategy using the features ranked based on Laplacian scores. The experimental results achieved on two publicly available data sets including all the forms of COVID-19 disease reveal that the method generalized well on unseen data. Moreover, 66.67%, 90.32%, and 100% sensitivity are obtained in the detection of mild, moderate, and severe cases, respectively.en_US
dc.identifier.doi10.1002/cpe.6802
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.scopus2-s2.0-85121467988en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1002/cpe.6802
dc.identifier.urihttps://hdl.handle.net/20.500.14034/368
dc.identifier.wosWOS:000734096300001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.journalConcurrency And Computation-Practice & Experienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectcomputer-aided diagnosisen_US
dc.subjectCOVID-19 detectionen_US
dc.subjectdeep featuresen_US
dc.subjectmilden_US
dc.subjectX-ray imagingen_US
dc.titleCOVID-19 detection with severity level analysis using the deep features, and wrapper-based selection of ranked featuresen_US
dc.typeArticleen_US

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