Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images

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dc.authorid0000-0002-8076-3678
dc.contributor.authorŞahinbaş, Kevser
dc.contributor.authorÇatak, Ferhat Özgür
dc.date.accessioned2023-03-09T13:04:03Z
dc.date.available2023-03-09T13:04:03Z
dc.date.issued2021
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü
dc.description.abstractCountries the world over have focused on protecting human health and combatting the COVID-19 outbreak. It has had a destructive effect on human health and daily life. Many people have been infected and have died. It is critical to control and prevent the spread of COVID-19 disease by applying quick alternative diagnostic techniques. Although laboratory tests have been widely applied as diagnostic tools, findings suggest that the application of X-ray and computed tomography images and pretrained deep convolutional neural network (CNN) models can help in the accurate detection of this disease. In this study, we propose a model for COVID-19 diagnosis, applying a deep CNN technique based on raw chest X-ray images of COVID-19 patients, which can be accessed publicly on GitHub. Fifty positive and 50 negative COVID-19 X-ray images for training and 20 positive and 20 negative images for testing phases are included. Because the classification of X-ray images needs a deep architecture to cope with the complicated structure of images, we apply five different architectures of well-known pretrained deep CNN models: VGG16, VGG19, ResNet, DenseNet, and InceptionV3. The pretrained VGG16 model can detect COVID-19 from non-COVID-19 cases with the highest classification performance of 80% accuracy among the other four proposed models, and it can be used as a helpful tool in the department of radiology. In the proposed model, a limited dataset of COVID-19 X-ray images is used that can provide more accurate performance when the number of instances in the dataset increases.
dc.identifier.citationŞahinbaş, K. ve Çatak, F. Ö. (2021). Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images. Data Science for COVID-19 Volume 1: Computational Perspectives içinde (451-466. ss.). Elsevier. https://dx.doi.org/10.1016/B978-0-12-824536-1.00003-4
dc.identifier.doi10.1016/B978-0-12-824536-1.00003-4
dc.identifier.endpage466
dc.identifier.isbn9780128245361
dc.identifier.scopus2-s2.0-85126078029
dc.identifier.scopusqualityN/A
dc.identifier.startpage451
dc.identifier.urihttps://dx.doi.org/10.1016/B978-0-12-824536-1.00003-4
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10597
dc.indekslendigikaynakScopus
dc.institutionauthorŞahinbaş, Kevser
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofData Science for COVID-19 Volume 1: Computational Perspectivesen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectChest X-Ray Images
dc.subjectCoronavirus (COVID-19)
dc.subjectDeep Learning
dc.subjectPretrained CNN Model
dc.subjectTransfer Learning
dc.titleTransfer learning-based convolutional neural network for COVID-19 detection with X-ray images
dc.typeBook Chapter

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