Enhanced dataset synthesis using conditional generative adversarial networks.

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Abstract

Biomedical 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.© Korean Society of Medical and Biological Engineering 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.