Enhanced dataset synthesis using conditional generative adversarial networks

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dc.authorid0000-0003-4236-3646
dc.contributor.authorMert, Ahmet
dc.date.accessioned2026-02-12T21:05:29Z
dc.date.available2026-02-12T21:05:29Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractBiomedical data acquisition, and reaching sufficient samples of participants are difficult and time ans effort consuming processes. On the other hand, the success rates of computer aided diagnosis (CAD) algorithms are sample and feature space depended. In this paper, conditional generative adversarial network (CGAN) based enhanced feature generation is proposed to synthesize large sample datasets having higher class separability. Twenty five percent of five medical datasets are used to train CGAN, and the synthetic datasets with any sample size are evaluated and compared to originals. Thus, new datasets can be generated with the help of the CGAN model and lower sample collection. It helps physicians decreasing sample collection processes, and it increases accuracy rates of the CAD systems using generated enhanced data with enhanced feature vectors. The synthesized datasets are classified using nearest neighbor, radial basis function support vector machine and artificial neural network to analyze the effectiveness of the proposed CGAN model.
dc.identifier.doi10.1007/s13534-022-00251-x
dc.identifier.endpage48
dc.identifier.issn2093-9868
dc.identifier.issn2093-985X
dc.identifier.issue1
dc.identifier.pmid36711160
dc.identifier.scopus2-s2.0-85142275490
dc.identifier.scopusqualityQ2
dc.identifier.startpage41
dc.identifier.urihttps://doi.org/10.1007/s13534-022-00251-x
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6976
dc.identifier.volume13
dc.identifier.wosWOS:000885871500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringernature
dc.relation.ispartofBiomedical Engineering Letters
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260212
dc.subjectGenerative adversarial network
dc.subjectFeature extraction
dc.subjectConditional GAN
dc.subjectDataset synthesis
dc.subjectDeep learning
dc.titleEnhanced dataset synthesis using conditional generative adversarial networks
dc.typeArticle

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