Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/3737
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dc.contributor.authorYaşar, H.-
dc.contributor.authorCeylan, M.-
dc.date.accessioned2023-03-03T13:34:25Z-
dc.date.available2023-03-03T13:34:25Z-
dc.date.issued2022-
dc.identifier.issn0127-9084-
dc.identifier.urihttps://doi.org/10.22452/mjcs.vol35no4.5-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/3737-
dc.description.abstractThe contagiousness rate of the COVID-19 virus, which was evaluated to have been transmitted from an animal to a human during the last months of 2019, is higher than the MERS-Cov and SARS-Cov viruses originating from the same family. The high rate of contagion has caused the COVID-19 virus to spread rapidly to all countries of the world. It is of great importance to be able to detect cases quickly in order to control the spread of the COVID-19 virus. Therefore, the development of systems that make automatic COVID-19 diagnoses using artificial intelligence approaches based on X-ray, CT scans, and ultrasound images are an urgent and indispensable requirement. In order to increase the number of X-ray images used within the study, a mixed data set was created by combining eight different data sets, thus maximizing the scope of the study. In the study, a total of 9,667 X-ray images were used, including 3,405 of COVID-19 samples, 2,780 of bacterial pneumonia samples, 1,493 of viral pneumonia samples and 1,989 of healthy samples. In this study, which aims to diagnose COVID-19 disease using X-ray images, automatic classification has been performed using two different classification structures: COVID-19 Pneumonia/Other Pneumonia/Healthy and COVID-19 Pneumonia/Bacterial Pneumonia/Viral Pneumonia/Healthy. Convolutional Neural Networks (CNNs), a successful deep learning method, were used as a classifier within the study. A total of seven CNN architectures were used: Mobilenetv2, Resnet101, Googlenet, Xception, Densenet201, Efficientnetb0, and Inceptionv3 architectures. The classification results were obtained from the original X-ray images, and the images were obtained by using Local Binary Pattern and Local Entropy. Then, new classification results were calculated from the obtained results using a pipeline algorithm. Detailed results were obtained to meet the scope of the study. According to the results of the experiments carried out, the three most successful CNN architectures for both three-class and four-class automatic classification were Densenet201, Xception, and Inceptionv3, respectively. In addition, it is understood that the pipeline algorithm used in the study is very useful for improving the results. The study results show that up to an improvement of 1.57% were achieved in some comparison parameters. © 2022, Malaysian Journal of Computer Science. All Rights Reserved.en_US
dc.description.sponsorshipThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.en_US
dc.language.isoenen_US
dc.publisherFaculty of Computer Science and Information Technologyen_US
dc.relation.ispartofMalaysian Journal of Computer Scienceen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolutional neural networksen_US
dc.subjectCovid-19en_US
dc.subjectDeep learningen_US
dc.subjectDensenet201en_US
dc.subjectInceptionv3en_US
dc.subjectLocal binary patternen_US
dc.subjectLocal entropyen_US
dc.subjectX-ray chest classificationen_US
dc.subjectXceptionen_US
dc.titleA NOVEL COMPARATIVE STUDY FOR AUTOMATIC THREE-CLASS AND FOUR-CLASS COVID-19 CLASSIFICATION ON X-RAY IMAGES USING DEEP LEARNINGen_US
dc.typeArticleen_US
dc.identifier.doi10.22452/mjcs.vol35no4.5-
dc.identifier.scopus2-s2.0-85141806534en_US
dc.departmentKTUNen_US
dc.identifier.volume35en_US
dc.identifier.issue4en_US
dc.identifier.startpage376en_US
dc.identifier.endpage402en_US
dc.identifier.wosWOS:001078530500005en_US
dc.institutionauthor-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanen_US
dc.authorscopusid56567916500-
dc.authorscopusid56276648900-
dc.identifier.scopusqualityQ4-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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