A comparison of deep learning models for pneumonia detection from chest x-ray images

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dc.authorid0000-0002-9048-6547en_US
dc.authorid0000-0001-9603-2231en_US
dc.authorid0000-0002-2696-8138en_US
dc.contributor.authorKadiroğlu, Zehra
dc.contributor.authorDeniz, Erkan
dc.contributor.authorŞenyiğit, Abdurrahman
dc.date.accessioned2024-03-25T06:45:19Z
dc.date.available2024-03-25T06:45:19Z
dc.date.issued2024en_US
dc.departmentDicle Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, Göğüs Hastalıkları Ana Bilim Dalıen_US
dc.description.abstractPneumonia is one of the acute lower respiratory tract diseases that can cause severe inflammation of the lung tissue. Although chest X-ray (CXR) is the most common clinical method for diagnosing pneumonia due to its low cost and ease of access, diagnosing pneumonia from CXR images is a difficult task even for specialist radiologists. It has been shown in the literature that deep learning-based image processing is effective in the automatic diagnosis of pneumonia. In conclusion, deep learning-based approaches were used in this study to classify pneumonia and healthy CXR images. These approaches are deep feature extraction, fine-tuning of pre-trained Convolutional Neural Networks (CNN), and end-to-end training of an enhanced ESA model. For deep feature extraction and transfer learning, 10 different pre-trained deep CNN models (AlexNet, ResNet50, DenseNet201, VGG16, VGG19, DarkNet53, ShuffleNet, Squeezenet, NASNetMobile and MobileNetV2) were used. Support Vector Machines (SVM), k Nearest Neighbor (kNN), Random Forest (RF) classifiers are used to classify deep features. The success of the fine-tuned AlexNet model produced an accuracy score of 98.50%, the highest of all results achieved. The end-to-end training of the developed ESA model yielded 96.75% results. The data set used in this study consists of Pneumonia and healthy CXR images obtained from Dicle University Medical Faculty Pulmonary Diseases and Tuberculosis clinic, intensive care unit and pulmonary outpatients’ clinic.en_US
dc.identifier.citationKadiroğlu, Z., Deniz, E. ve Şenyiğit, A. (2024). A comparison of deep learning models for pneumonia detection from chest x-ray images. Journal of the Faculty of Engineering and Architecture of Gazi University, 39(2), 729-740.en_US
dc.identifier.doi10.17341/gazimmfd.1204092en_US
dc.identifier.endpage740en_US
dc.identifier.issn1300-1884
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85180156403en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage729en_US
dc.identifier.trdizinid1246506en_US
dc.identifier.urihttps://dergipark.org.tr/tr/pub/gazimmfd/issue/80437/1204092
dc.identifier.urihttps://hdl.handle.net/11468/13689
dc.identifier.volume39en_US
dc.identifier.wosWOS:001117961500007
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorŞenyiğit, Abdurrahman
dc.language.isoenen_US
dc.publisherGazi Universitesien_US
dc.relation.ispartofJournal of the Faculty of Engineering and Architecture of Gazi Universityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectChest x-ray imagesen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDeep feature extractionen_US
dc.subjectPneumonia detectionen_US
dc.subjectTransfer learningen_US
dc.titleA comparison of deep learning models for pneumonia detection from chest x-ray imagesen_US
dc.title.alternativeGöğüs röntgen görüntülerinde pnömoni tespiti için derin öğrenme modellerinin karşılaştırılmasıen_US
dc.typeArticleen_US

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