A Hybrid Capsule Network for Pneumonia Detection Using Image Augmentation Based on Generative Adversarial Network

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dc.authoridTalu, Muhammed Fatih/0000-0003-1166-8404
dc.authoridFırıldak, Kazım/0000-0002-1958-3627
dc.authorwosidTalu, Muhammed Fatih/W-2834-2017
dc.authorwosidFırıldak, Kazım/AAR-8770-2021
dc.contributor.authorFirildak, Kazim
dc.contributor.authorTalu, Muhammed Fatih
dc.date.accessioned2024-08-04T20:50:29Z
dc.date.available2024-08-04T20:50:29Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractPneumonia, featured by inflammation of the air sacs in one or both lungs, is usually detected by examining chest X-ray images. This paper probes into the classification models that can distinguish between normal and pneumonia images. As is known, trained networks like AlexNet and GoogleNet are deep network architectures, which are widely adopted to solve many classification problems. They have been adapted to the target datasets, and employed to classify new data generated through transfer learning. However, the classical architectures are not accurate enough for the diagnosis of pneumonia. Therefore, this paper designs a capsule network with high discrimination capability, and trains the network on Kaggle' s online pneumonia dataset, which contains chest X-ray images of many adults and children. The original dataset consists of 1,583 normal images, and 4,273 pneumonia images. Then, two data augmentation approaches were applied to the dataset, and their effects on classification accuracy were compared in details. The model parameters were optimized through five different experiments. The results show that the highest classification accuracy (93.91% even on small images) was achieved by the capsule network, coupled with data augmentation by generative adversarial network (GAN), using optimized parameters. This network outperformed the classical strategies.en_US
dc.identifier.doi10.18280/ts.380309
dc.identifier.endpage627en_US
dc.identifier.issn0765-0019
dc.identifier.issn1958-5608
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85111718417en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage619en_US
dc.identifier.urihttps://doi.org/10.18280/ts.380309
dc.identifier.urihttps://hdl.handle.net/11616/100093
dc.identifier.volume38en_US
dc.identifier.wosWOS:000681761900009en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInt Information & Engineering Technology Assocen_US
dc.relation.ispartofTraitement Du Signalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectpneumoniaen_US
dc.subjectcapsule networken_US
dc.subjectdeep convolutional generative adversarial network (DCGAN)en_US
dc.subjectchest X-rayen_US
dc.subjectdata augmentationen_US
dc.subjectclassificationen_US
dc.titleA Hybrid Capsule Network for Pneumonia Detection Using Image Augmentation Based on Generative Adversarial Networken_US
dc.typeArticleen_US

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