Abstract
In recent years, many papers and models have been developed to study the classification of X-ray images of lung diseases. The use of transfer learning, which allows using already trained network models for new problems, could allow for better results in the COVID-19 disease classification problem. However, at the beginning of the pandemic, there were not very large databases of SARS-CoV-2 positive patient images on which a network could perform learning. A solution to this problem could be a Generative Adversarial Network (GAN) algorithm to create new synthetic data indistinguishable from the real data using the available data set. It would allow training a network capable of performing classification with greater accuracy on a larger and more diverse number of training data. Obtaining such a tool could allow for more efficient research on how to solve the global COVID-19 pandemic problem. The research presented in this paper aims to investigate the impact of using a Generative Adversarial Network for COVID-19-related imaging diagnostics in the classification problem using transfer learning.
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Acknowledgements
This work is supported in part by the Research Fund of Department of Systems and Computer Networks, Faculty of ICT, Wroclaw University of Science and Technology and by the CEUS-UNISONO programme, with funding from the National Science Centre, Poland under grant agreement No. 2020/02/Y/ST6/00037.
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Dereń, J., Woźniak, M. (2022). Employing Generative Adversarial Network in COVID-19 Diagnosis. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_20
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