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,一个叫COVID-19的新奇病毒造成了大量伤亡。与科罗纳病毒的战斗在2019年西班牙流感之后令人困惑和惊恐。一线医生和医学研究人员在控制高度毗连病毒的传播方面取得了重大进展,但技术也证明了其在战斗中的重要性。此外,在许多医学应用中采用了人工智能来诊断许多疾病,甚至令人困惑的有经验的医生。因此,本调查文件探讨了在早期和廉价的疾病诊断方法方面可以帮助医生和研究人员的拟议方法。大多数发展中国家都难以用常规方式进行测试,但机器和深层学习可以采用一种重要的方式。另一方面,获得不同类型医学图像的机会也激发了研究人员的动力。因此,提出了大量技术。本文首先详细介绍了人工智能领域常规方法的背景知识。随后,我们收集了常用的数据集及其迄今为止的使用案例。此外,我们还展示了研究人员在采用机器学习而不是深层学习方面的百分比,但是在机器学习和深深深层学习方面也可以采用一种重要的方式。我们从研究到深层研究中找出了我们所面临的问题。我们最后在研究中提出了一种深刻的课题和深层次的课题。