Application of Generative Adversarial Networks to Improve COVID-19 Classification on Ultrasound Images

COVID-19 screening is crucial for the early diagnosis and treatment of the disease, with lung ultrasound posing as a cost-effective alternative to other imaging techniques. Given the dependency on medical expertise and experience to accurately identify patterns in ultrasound exams, deep learning techniques have been explored for automatically classifying patients' conditions. However, the limited availability of public medical databases remains a significant obstacle to the development of more advanced models. To address the data scarcity problem, this study proposes a method that leverages generative adversarial networks (GANs) to generate synthetic lung ultrasound images, which are subsequently used to train frame-based classification models. Two types of GANs are considered: Wasserstein GANs (WGAN) and Pix2Pix. Specific tools are used to show that the synthetic data produced present a distribution close to the original data. The classification models trained with synthetic data achieved a peak accuracy of 96.32% ± 4.17%, significantly outperforming the maximum accuracy of 82.69% ± 10.42% obtained when training only with the original data. Furthermore, the best results are comparable to, and in some cases surpass, those reported in recent related studies.