A new approach for Pap-Smear image generation with generative adversarial networks

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dc.authoridTalu, Muhammed Fatih/0000-0003-1166-8404
dc.authoridALTUN GUVEN, SARA/0000-0003-2877-7105
dc.authorwosidTalu, Muhammed Fatih/W-2834-2017
dc.contributor.authorAltun, Sara
dc.contributor.authorTalu, Muhammed Fatih
dc.date.accessioned2024-08-04T20:10:11Z
dc.date.available2024-08-04T20:10:11Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractPurpose: In this study, a new GAN model for histopathological Pap-Smear images generation is suggested. To illustrate the advantage of the proposed GAN model (Pix2PixSSIM), a comprehensive experimental study has been carried out. Theory and Methods: Pix2PixSSIM designed as generative adversarial networks model for histopathological Pap-Smear images generation. In addition, the proposed model compared with the existing GANs (i.e., Pix2Pix, CycleGAN, DiscoGAN and AttentionGAN). Results: In experimental studies, Pix2PixSSIM, which is designed as generative adversarial networks model for histopathological Pap-Smear images generation, has shown high accuracy than other methods. Conclusion: It is seen that the performance of the proposed GAN architecture to produce patterns similar to real Pap-Smear visuals gives successful results (MSI=23.649, PSNR=37.476) when compared to existing approaches (MathModel and classical image synthesis methods).en_US
dc.identifier.doi10.17341/gazimmfd.939092
dc.identifier.endpage1410en_US
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85128740867en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1401en_US
dc.identifier.trdizinid508632en_US
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.939092
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/508632
dc.identifier.urihttps://hdl.handle.net/11616/92644
dc.identifier.volume37en_US
dc.identifier.wosWOS:000834843300020en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherGazi Univ, Fac Engineering Architectureen_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.subjectPap-Smearen_US
dc.subjectImage generationen_US
dc.subjectDeep learningen_US
dc.subjectConvolutional neural networken_US
dc.subjectGenerative Adversarial Networksen_US
dc.titleA new approach for Pap-Smear image generation with generative adversarial networksen_US
dc.typeArticleen_US

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