Heidari Arash, Jafari Navimipour Nima, Unal Mehmet, Toumaj Shiva
Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran.
Neural Comput Appl. 2022;34(18):15313-15348. doi: 10.1007/s00521-022-07424-w. Epub 2022 Jun 10.
Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.
最近，新冠疫情已导致数百万人死亡，并几乎影响到人类生活的各个领域。机器学习（ML）方法在医学领域的许多应用中得到了应用，包括对患者的检测和监测，尤其是在新冠疫情管理方面。不同的医学成像系统，如计算机断层扫描（CT）和X射线，为ML抗击疫情提供了一个绝佳的平台。由于这种需求，已经开展了大量的研究；因此，在这项工作中，我们采用了系统文献综述（SLR）来涵盖相关论文成果的各个方面。成像方法、生存分析、预测、经济和地理问题、监测方法、药物研发以及混合应用是新冠疫情中所采用应用的七个关键用途。传统神经网络（CNN）、长短期记忆网络（LSTM）、循环神经网络（RNN）、生成对抗网络（GAN）、自动编码器、随机森林和其他ML技术经常用于此类场景。接下来，将讨论与大流行医疗问题的ML技术相关的前沿应用。回顾了与ML应用于此次疫情相关的各种问题和挑战。预计在未来将进行更多研究以限制疫情传播和进行灾难管理。根据数据，大多数论文主要根据灵活性和准确性等特征进行评估，而诸如安全性等其他因素则被忽视。此外，在所研究的论文中，Keras是最常使用的库，使用时间占比24.4%。此外，在20.4%的应用中，医学成像系统用于诊断目的。
