PneumoNet: Automated Detection of Pneumonia using Deep Neural Networks from Chest X-Ray Images

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dc.contributor.authorKadıroglu, Zehra
dc.contributor.authorDenız, Erkan
dc.contributor.authorKayaoglu, Mazhar
dc.contributor.authorGuldemır, Hanifi
dc.contributor.authorSenyıgıt, Abdurrahman
dc.contributor.authorSengur, Abdulkadir
dc.date.accessioned2025-02-22T14:13:27Z
dc.date.available2025-02-22T14:13:27Z
dc.date.issued2024
dc.departmentDicle Üniversitesien_US
dc.description.abstractPneumonia is a dangerous disease that causes severe inflammation of the air sacs in the lungs. It is one of the infectious diseases with high morbidity and mortality in all age groups worldwide. Chest X-ray (CXR) is a diagnostic and imaging modality widely used in diagnosing pneumonia due to its low dose of ionizing radiation, low cost, and easy accessibility. Many deep learning methods have been proposed in various medical applications to assist clinicians in detecting and diagnosing pneumonia from CXR images. We have proposed a novel PneumoNet using a convolutional neural network (CNN) to detect pneumonia using CXR images accurately. Transformer-based deep learning methods, which have yielded high performance in natural language processing (NLP) problems, have recently attracted the attention of researchers. In this work, we have compared our results obtained using the CNN model with transformer-based architectures. These transformer architectures are vision transformer (ViT), gated multilayer perceptron (gMLP), MLP-mixer, and FNet. In this study, we have used the healthy and pneumonia CXR images from public and private databases to develop the model. Our developed PneumoNet model has yielded the highest accuracy of 96.50% and 94.29% for private and public databases, respectively, in detecting pneumonia accurately from healthy subjects.en_US
dc.identifier.doi10.55525/tjst.1411197
dc.identifier.endpage338en_US
dc.identifier.issn1308-9099
dc.identifier.issue2en_US
dc.identifier.startpage325en_US
dc.identifier.trdizinid1269972en_US
dc.identifier.urihttps://doi.org/10.55525/tjst.1411197
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1269972
dc.identifier.urihttps://hdl.handle.net/11468/29963
dc.identifier.volume19en_US
dc.indekslendigikaynakTR-Dizin
dc.language.isoenen_US
dc.relation.ispartofTurkish Journal of Science & Technologyen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_TR_20250222
dc.subjectDeep neural networksen_US
dc.subjecttransformeren_US
dc.subjectPneumonia detectionen_US
dc.subjectmedical image classificationen_US
dc.subjectchest x-ray imagingen_US
dc.titlePneumoNet: Automated Detection of Pneumonia using Deep Neural Networks from Chest X-Ray Imagesen_US
dc.typeArticleen_US

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