Implementation of convolutional neural network approach for COVID-19 disease detection

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dc.contributor.authorIrmak, Emrah
dc.date.accessioned2021-02-19T21:20:51Z
dc.date.available2021-02-19T21:20:51Z
dc.date.issued2020
dc.departmentALKÜ
dc.description.abstractIn this paper, two novel, powerful, and robust convolutional neural network (CNN) architectures are designed and proposed for two different classification tasks using publicly available data sets. The first architecture is able to decide whether a given chest X-ray image of a patient contains COVID-19 or not with 98.92% average accuracy. The second CNN architecture is able to divide a given chest X-ray image of a patient into three classes (COVID-19 versus normal versus pneumonia) with 98.27% average accuracy. The hyperparameters of both CNN models are automatically determined using Grid Search. Experimental results on large clinical data sets show the effectiveness of the proposed architectures and demonstrate that the proposed algorithms can overcome the disadvantages mentioned above. Moreover, the proposed CNN models are fully automatic in terms of not requiring the extraction of diseased tissue, which is a great improvement of available automatic methods in the literature. To the best of the author’s knowledge, this study is the first study to detect COVID-19 disease from given chest X-ray images, using CNN, whose hyperparameters are automatically determined by the Grid Search. Another important contribution of this study is that it is the first CNN-based COVID-19 chest X-ray image classification study that uses the largest possible clinical data set. A total of 1,524 COVID-19, 1,527 pneumonia, and 1524 normal Xray images are collected. It is aimed to collect the largest number of COVID-19 X-ray images that exist in the literature until the writing of this research paper. © 2020 the American Physiological Society.
dc.identifier.doi10.1152/physiolgenomics.00084.2020
dc.identifier.endpage601en_US
dc.identifier.issn1094-8341
dc.identifier.issue12en_US
dc.identifier.pmid33094700
dc.identifier.scopusqualityQ2
dc.identifier.startpage590en_US
dc.identifier.urihttps://doi.org/10.1152/physiolgenomics.00084.2020
dc.identifier.urihttps://hdl.handle.net/20.500.12868/733
dc.identifier.volume52en_US
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorIrmak, Emrah
dc.language.isoen
dc.publisherAmerican Physiological Society
dc.relation.ispartofPhysiological Genomics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectConvolutional neural network
dc.subjectCOVID-19 detection
dc.subjectDeep learning
dc.subjectImage classification
dc.subjectMedical image processing
dc.titleImplementation of convolutional neural network approach for COVID-19 disease detection
dc.typeArticle

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