COVID-19 (Coronavirus disease 2019) has been quickly spreading since its outbreak, impacting financial markets and healthcare systems globally. Countries all around the world have adopted a number of extraordinary steps to restrict the spreading virus, where early COVID-19 diagnosis is essential. Medical images such as X-ray images and Computed Tomography scans are becoming one of the main diagnostic tools to combat COVID-19 with the aid of deep learning-based systems. In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis. We also provide a summary of the X-ray and CT image datasets used in various studies for COVID-19 detection. The key difficulties and potential applications of deep learning in fighting against COVID-19 are finally discussed. This work summarizes the latest methods of deep learning using medical images to diagnose COVID-19, highlighting the challenges and inspiring more studies to keep utilizing the advantages of deep learning to combat COVID-19.

中文翻译：

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用于 COVID-19 诊断的深度学习和医学成像：综合调查

COVID-19（2019 年冠状病毒病）自爆发以来迅速传播，影响了全球金融市场和医疗保健系统。世界各国都采取了一些非常措施来限制病毒的传播，其中 COVID-19 的早期诊断至关重要。在基于深度学习的系统的帮助下，X 射线图像和计算机断层扫描等医学图像正在成为对抗 COVID-19 的主要诊断工具之一。在本次调查中，我们从图像分类、病变定位和严重程度量化等方面调查了使用医学图像的深度学习应用在对抗 COVID-19 中的主要贡献，并回顾了不同的深度学习架构和一些图像预处理技术，以实现更精确的诊断。我们还提供了在各种 COVID-19 检测研究中使用的 X 射线和 CT 图像数据集的摘要。最后讨论了深度学习在对抗 COVID-19 中的关键困难和潜在应用。这项工作总结了使用医学图像诊断 COVID-19 的深度学习的最新方法，强调了挑战并激发了更多的研究，以继续利用深度学习的优势来对抗 COVID-19。