An Application on Chest X-Ray Images for the Detection of Tuberculosis Disease by Employing Deep Convolutional Neural Networks

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dc.contributor.authorKoç, Hatice
dc.contributor.authorHızıroğlu, Kadir
dc.contributor.authorErbaycu, Ahmet Emin
dc.date.accessioned2025-03-21T07:38:22Z
dc.date.available2025-03-21T07:38:22Z
dc.date.issued2023
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractTuberculosis is the second infectious disease causing death after COVID-19. Diagnosing it is an easy and cheap via chest radiographs. However, some countries lack medical personnel and equipment for tuberculosis detection on chest radiographs. Computer-aided diagnosis and computer-aided detection systems utilizing deep learning can be employed to identify tuberculosis on medical images. Although there are some studies, they are insufficient for unbiased systems because these systems require the datasets having different features. The aim of this study is to evaluate the performance of pretrained networks for a classification application on chest X-ray images by utilizing the dataset from the Hospital in Turkey and Montgomery Count Dataset. The predictive models were implemented with the pre-trained DCNNs such as ResNet-50, Xception, and GoogLeNet. An Xception model provides the best performance.
dc.identifier.endpage64
dc.identifier.issn2757-9778
dc.identifier.issue1
dc.identifier.startpage51
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2760
dc.identifier.volume3
dc.language.isoen
dc.publisherİzmir Bakırçay Üniversitesi
dc.relation.ispartofArtificial Intelligence Theory and Applications
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20250319
dc.subjecttuberculosis
dc.subjectdeep convolutional neural networks
dc.subjecttransfer learning
dc.subjectclassification
dc.titleAn Application on Chest X-Ray Images for the Detection of Tuberculosis Disease by Employing Deep Convolutional Neural Networks
dc.typeArticle

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