Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:2058-2061. doi: 10.1109/EMBC48229.2022.9871519.
The novel coronavirus infection (COVID-19) is still continuing to be a concern for the entire globe. Since early detection of COVID-19 is of particular importance, there have been multiple research efforts to supplement the current standard RT-PCR tests. Several deep learning models, with varying effectiveness, using Chest X-Ray images for such diagnosis have also been proposed. While some of the models are quite promising, there still remains a dearth of training data for such deep learning models. The present paper attempts to provide a viable solution to the problem of data deficiency in COVID-19 CXR images. We show that the use of a Wasserstein Generative Adversarial Network (WGAN) could lead to an effective and lightweight solution. It is demonstrated that the WGAN generated images are at par with the original images using inference tests on an already proposed COVID-19 detection model.
新型冠状病毒感染（COVID-19）仍然是全球关注的焦点。由于早期检测 COVID-19 尤为重要，因此已经有多项研究工作来补充当前的标准 RT-PCR 检测。已经提出了几种使用 X 光胸片进行此类诊断的深度学习模型，这些模型的有效性也有所不同。虽然其中一些模型非常有前途，但此类深度学习模型仍然缺乏训练数据。本文试图为 COVID-19 CXR 图像的数据不足问题提供一个可行的解决方案。我们表明，使用 Wasserstein 生成对抗网络（WGAN）可以提供有效的轻量级解决方案。通过对已经提出的 COVID-19 检测模型进行推理测试，证明了 WGAN 生成的图像与原始图像相当。
