An empirical evaluation of generative adversarial nets in synthesizing X-ray chest images

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dc.contributor.authorBelmekki, Zakariae
dc.contributor.authorLi, Jun
dc.contributor.authorJenkins, Karl W.
dc.contributor.authorReuter, Patrick
dc.contributor.authorGómez Jáuregui, David Antonio
dc.date.accessioned2023-01-12T13:30:24Z
dc.date.available2023-01-12T13:30:24Z
dc.date.issued2022-12-12
dc.description.abstractIn the last decade, Generative Adversarial Nets (GAN) have become a subject of growing interest in multiple research fields. In this paper, we focus on applications in the medical field by attempting to generate realistic X-ray chest images. A heuristic approach is adopted to perform an extensive evaluation of different architecture configurations and optimization algorithms and we propose an optimal configuration of the baseline Deep Convolutional GAN (DCGAN) based on empirical findings. Additionally, we highlight the technical limitations of GAN and provide an analysis of the high memory requirements, which we reduce by a range of 1.2-7 percent by removing unnecessary layers. Finally, we verify that the loss of the discriminator can be used as an assessment metric.en_UK
dc.identifier.citationBelmekki Z, Li J, Jenkins K, et al., (2022) An empirical evaluation of generative adversarial nets in synthesizing X-ray chest images. In: 2022 7th International Conference on Intelligent Informatics and Biomedical Science (ICIIBMS), 24-26 November 2022 Nara, Japanen_UK
dc.identifier.eisbn978-1-6654-8229-5
dc.identifier.isbn978-1-6654-8230-1
dc.identifier.issn2189-8723
dc.identifier.urihttps://doi.org/10.1109/ICIIBMS55689.2022.9971542
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18932
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleAn empirical evaluation of generative adversarial nets in synthesizing X-ray chest imagesen_UK
dc.typeConference paperen_UK

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