Covid-19 is now one of the most incredibly intense and severe illnesses of the twentieth century. Covid-19 has already endangered the lives of millions of people worldwide due to its acute pulmonary effects. Image-based diagnostic techniques like X-ray, CT, and ultrasound are commonly employed to get a quick and reliable clinical condition. Covid-19 identification out of such clinical scans is exceedingly time-consuming, labor-intensive, and susceptible to silly intervention. As a result, radiography imaging approaches using Deep Learning (DL) are consistently employed to achieve great results. Various artificial intelligence-based systems have been developed for the early prediction of coronavirus using radiography pictures. Specific DL methods such as CNN and RNN noticeably extract extremely critical characteristics, primarily in diagnostic imaging. Recent coronavirus studies have used these techniques to utilize radiography image scans significantly. The disease, as well as the present pandemic, was studied using public and private data. A total of 64 pre-trained and custom DL models concerning imaging modality as taxonomies are selected from the studied articles. The constraints relevant to DL-based techniques are the sample selection, network architecture, training with minimal annotated database, and security issues. This includes evaluating causal agents, pathophysiology, immunological reactions, and epidemiological illness. DL-based Covid-19 detection systems are the key focus of this review article. Covid-19 work is intended to be accelerated as a result of this study.

Covid-19 现在是二十世纪最严重、最严重的疾病之一。由于其严重的肺部影响，Covid-19 已经危及全世界数百万人的生命。基于图像的诊断技术（如 X 射线、CT 和超声波）通常用于获得快速可靠的临床状况。从此类临床扫描中识别 Covid-19 非常耗时、费力，并且容易受到愚蠢的干预。因此，始终采用使用深度学习 (DL) 的放射线成像方法来取得良好的结果。已经开发了各种基于人工智能的系统，用于使用放射线照片早期预测冠状病毒。 CNN 和 RNN 等特定的深度学习方法明显提取了极其关键的特征，主要是在诊断成像中。最近的冠状病毒研究已经使用这些技术来显着利用放射线照相图像扫描。使用公共和私人数据对这种疾病以及当前的大流行进行了研究。从研究文章中选择了总共 64 个关于成像模式作为分类法的预训练和定制 DL 模型。与基于深度学习的技术相关的限制包括样本选择、网络架构、使用最少的注释数据库进行训练以及安全问题。这包括评估致病因素、病理生理学、免疫反应和流行病学疾病。基于深度学习的 Covid-19 检测系统是本文的重点。这项研究旨在加速 Covid-19 工作。