In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. The battle with the novel Coronavirus is baffling and horrifying after the Spanish Flu 2019. While the front-line doctors and medical researchers have made significant progress in controlling the spread of the highly contiguous virus, technology has also proved its significance in the battle. Moreover, Artificial Intelligence has been adopted in many medical applications to diagnose many diseases, even baffling experienced doctors. Therefore, this survey paper explores the methodologies proposed that can aid doctors and researchers in early and inexpensive methods of diagnosis of the disease. Most developing countries have difficulties carrying out tests using the conventional manner, but a significant way can be adopted with Machine and Deep Learning. On the other hand, the access to different types of medical images has motivated the researchers. As a result, a mammoth number of techniques are proposed. This paper first details the background knowledge of the conventional methods in the Artificial Intelligence domain. Following that, we gather the commonly used datasets and their use cases to date. In addition, we also show the percentage of researchers adopting Machine Learning over Deep Learning. Thus we provide a thorough analysis of this scenario. Lastly, in the research challenges, we elaborate on the problems faced in COVID-19 research, and we address the issues with our understanding to build a bright and healthy environment.

翻译：2019年12月，一种名为COVID-19的新型病毒至今已造成巨大伤亡。自2019年西班牙流感以来，与新型冠状病毒的斗争令人困惑且恐惧。虽然一线医生和医学研究人员在控制这种高传染性病毒的传播方面取得了重大进展，但技术也在这场战斗中证明了其重要性。此外，人工智能已被广泛应用于多种医疗应用领域，用于诊断诸多疾病，甚至是连经验丰富的医生都感到棘手的病症。因此，本篇综述论文探讨了所提出的方法论，这些方法能够帮助医生和研究人员以早期且低成本的方式诊断该疾病。大多数发展中国家难以采用常规方式进行检测，但利用机器学习和深度学习可以采取一种重要的途径。另一方面，对不同类型医学图像的可及性也激励了研究人员。因此，大量技术被提出。本文首先详述了人工智能领域传统方法的背景知识。随后，我们收集了迄今为止常用的数据集及其使用案例。此外，我们还展示了研究人员采用机器学习相对于深度学习的比例。由此，我们对这一现状进行了透彻的分析。最后，在研究挑战部分，我们阐述了COVID-19研究中面临的问题，并基于我们的理解指出了构建一个光明健康环境所需解决的议题。