Iglesias Morís, DanielGende, M.Moura, Joaquim deNovo Buján, JorgeOrtega Hortas, Marcos2024-05-142024-05-142022Morís, D.I., Gende, M., de Moura, J., Novo, J., Ortega, M. (2022). Performance Analysis of GAN Approaches in the Portable Chest X-Ray Synthetic Image Generation for COVID-19 Screening. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2022. EUROCAST 2022. Lecture Notes in Computer Science, vol 13789. Springer, Cham. https://doi.org/10.1007/978-3-031-25312-6_47978-3-031-25312-6http://hdl.handle.net/2183/36470Versión aceptada de: Morís, D.I., Gende, M., de Moura, J., Novo, J., Ortega, M. (2022). Performance Analysis of GAN Approaches in the Portable Chest X-Ray Synthetic Image Generation for COVID-19 Screening. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2022. EUROCAST 2022. Lecture Notes in Computer Science, vol 13789. Springer, Cham. https://doi.org/10.1007/978-3-031-25312-6_47[Abstract]: COVID-19 mainly affects lung tissues, aspect that makes chest X-ray imaging useful to visualize this damage. In the context of the global pandemic, portable devices are advantageous for the daily practice. Furthermore, Computer-aided Diagnosis systems developed with Deep Learning algorithms can support the clinicians while making decisions. However, data scarcity is an issue that hinders this process. Thus, in this work, we propose the performance analysis of 3 different stateof-the-art Generative Adversarial Networks (GAN) approaches that are used for synthetic image generation to improve the task of automatic COVID-19 screening using chest X-ray images provided by portable devices. Particularly, the results demonstrate a significant improvement in terms of accuracy, that raises 5.28% using the images generated by the best image translation model.eng© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AGComputer-aided DiagnosisPortable Chest X-rayCOVID-19Deep LearningSynthetic Image Generationconference outputopen accesshttps://doi.org/10.1007/978-3-031-25312-6_47