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:在2019年12月,一种名为COVID-19的新病毒已经造成了巨大数量的死亡。与西班牙流感2019年相比,与新冠病毒的抗战令人困惑和恐惧。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,但技术在战斗中也证明了其重要性。此外,人工智能已被应用于许多医疗应用程序中,用于诊断许多疾病,甚至是经验丰富的医生也难以理解的疾病。因此,本综述论文探讨了提出的可以帮助医生和研究人员进行早期和廉价的疾病诊断的方法。大多数发展中国家在使用传统方法进行测试方面存在困难,但可以采用机器学习和深度学习的重要方法。另一方面,获取不同类型的医学图像已经激发了研究人员的积极性。因此提出了大量的技术。本文首先详细介绍了人工智能领域的传统方法的背景知识。随后,我们收集常用的数据集及其迄今为止的用例。此外,我们还展示了采用机器学习而不是深度学习的研究人员的比例。因此,我们对这种情况进行了全面的分析。最后,在研究挑战方面,我们详细阐述了COVID-19研究面临的问题,并提出了我们自己的理解解决这些问题,建立一个明亮和健康的环境。