Resizing and cleaning of histopathological images using generative adversarial networks

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dc.authoridTalu, Muhammed Fatih/0000-0003-1166-8404
dc.authorwosidTalu, Muhammed Fatih/W-2834-2017
dc.contributor.authorCelik, Gaffari
dc.contributor.authorTalu, Muhammed Fatih
dc.date.accessioned2024-08-04T20:47:14Z
dc.date.available2024-08-04T20:47:14Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBilinear and Bicubic interpolation techniques are frequently used to increase image resolution. These techniques with data modeling approach are replaced by intelligent systems that can learn automatically from data. SRGAN is a modern Generative Adversarial Network developed as an alternative to classical interpolation techniques. His ability to produce images in super resolution has attracted the attention of many researchers. In this study, noise elimination performance of super resolution generative adversarial network (SRGAN) with image magnification was investigated. The results of the noise cleaning were compared with the classical approaches (mean, median, adaptive filters). SSIM, PSNR and FFT_MSE metrics were evaluated in experimental studies using images in the data set Camelyon17. When the results were evaluated, it was observed that SRGAN was superior to the classical approaches not only in increasing the resolution but also in the noise cleaning area. (C) 2019 Published by Elsevier B.V.en_US
dc.identifier.doi10.1016/j.physa.2019.122652
dc.identifier.issn0378-4371
dc.identifier.issn1873-2119
dc.identifier.scopus2-s2.0-85083291576en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.physa.2019.122652
dc.identifier.urihttps://hdl.handle.net/11616/99249
dc.identifier.volume554en_US
dc.identifier.wosWOS:000540727200002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofPhysica A-Statistical Mechanics and Its Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSRGANen_US
dc.subjectNoise cleaningen_US
dc.subjectImage resizingen_US
dc.subjectBicubicen_US
dc.subjectCamelyon17en_US
dc.titleResizing and cleaning of histopathological images using generative adversarial networksen_US
dc.typeArticleen_US

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