Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/225
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dc.contributor.authorBarstuğan, Mücahid-
dc.contributor.authorÖzkaya, U.-
dc.contributor.authorÖztürk, Ş.-
dc.date.accessioned2021-12-13T10:23:53Z-
dc.date.available2021-12-13T10:23:53Z-
dc.date.issued2021-
dc.identifier.issn1613-0073-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/225-
dc.description4th International Conference on Recent Trends and Applications in Computer Science and Information Technology, RTA-CSIT 2021 -- 21 May 2021 through 22 May 2021 -- -- 169296en_US
dc.description.abstractThis study detected the Coronavirus (COVID-19) disease by implementing artificial learning methods. Coronavirus disease occurs in the lungs and can cause death. The detection process was performed on chest Computed Tomography (CT) images. The training process was implemented by using 32x32 patches that were obtained from CT images. This study includes three phases: The first phase classifies patches by the SVM algorithm without implementing the feature extraction methods. The second phase extracts features on patches by using Grey Level Co-occurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRLM), Grey-Level Size Zone Matrix (GLSZM), Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT), and Discrete Cosine Transform (DCT) methods and classifies the features extracted. The third phase uses Convolutional Neural Networks (CNN) method to classify the patches. 10-fold cross-validation is implemented in the classification process. The sensitivity, specificity, accuracy, precision, and F-score metrics measure the classification performance. The highest classification accuracy was achieved as 99.15% by the CNN method during the training process. The classification structure, which has the highest classification accuracy, was used during the test performance and had 80.21% mean sensitivity rate, which is the COVID detection performance, on 727 test images. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).en_US
dc.language.isoenen_US
dc.publisherCEUR-WSen_US
dc.relation.ispartofCEUR Workshop Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectCoronavirusen_US
dc.subjectCOVID-19en_US
dc.subjectCT imagesen_US
dc.subjectDeep Learningen_US
dc.subjectFeature extractionen_US
dc.subjectMachine learningen_US
dc.titleCoronavirus (Covid-19) classification using CT images by machine learning methodsen_US
dc.typeConference Objecten_US
dc.identifier.scopus2-s2.0-85107831990en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.volume2872en_US
dc.identifier.startpage29en_US
dc.identifier.endpage35en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.authorscopusid57200139642-
dc.authorscopusid57191610477-
dc.authorscopusid57191953654-
dc.identifier.scopusquality--
item.openairetypeConference Object-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
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