Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier

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dc.authorscopusid55807379700
dc.contributor.authorSaygılı, Ahmet
dc.date.accessioned2022-05-11T14:15:58Z
dc.date.available2022-05-11T14:15:58Z
dc.date.issued2022
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractCOVID-19 is a virus that has been declared an epidemic by the world health organization and causes more than 2 million deaths in the world. To achieve this, computer-aided automatic diagnosis systems are created on medical images. In this study, an image processing and machine learning-based method is proposed that enables segmenting of CT images taken from COVID-19 patients and automatic detection of the virus through the segmented images. The main purpose of the study is to automatically diagnose the COVID-19 virus. The study consists of three basic steps: preprocessing, segmentation and classification. Image resizing, image sharpening, noise removal, contrast stretching processes are included in the preprocessing phase and segmentation of images with Expectation–Maximization-based Gaussian Mixture Model in the segmentation phase. In the classification stage, COVID-19 is classified as positive and negative by using kNN, decision tree, and two different ensemble methods together with the kernel support vector machines method. In the study, two different CT datasets that are open to the public and a mixed dataset created by combining these datasets were used. The best accuracy values for Dataset-1, Dataset-2 and Mixed Dataset are 98.5%, 86.3%, 94.5%, respectively. The achieved results prove that the proposed approach advances state-of-the-art performance. Within the scope of the study, a GUI that can automatically detect COVID-19 has been created. © 2021, King Fahd University of Petroleum & Minerals.
dc.description.sponsorship21.317, NKUBAP.06
dc.description.sponsorshipThis work was supported by Research Fund of the Tekirdag Nam?k Kemal University. Project Number: NKUBAP.06.GA.21.317
dc.description.sponsorshipThis work was supported by Research Fund of the Tekirdag Namık Kemal University. Project Number: NKUBAP.06.GA.21.317
dc.identifier.doi10.1007/s13369-021-06240-z
dc.identifier.endpage2453
dc.identifier.issn2193-567X
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85116642308
dc.identifier.scopusqualityQ1
dc.identifier.startpage2435
dc.identifier.urihttps://doi.org/10.1007/s13369-021-06240-z
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6137
dc.identifier.volume47
dc.indekslendigikaynakScopus
dc.institutionauthorSaygılı, Ahmet
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofArabian Journal for Science and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectClassification
dc.subjectCOVID-19
dc.subjectExpectation–Maximization
dc.subjectGMM
dc.subjectSegmentation
dc.titleComputer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier
dc.typeArticle

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