The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers’ interest. This survey marks a detailed inspection of the deep learning–based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets and others, along with probable solutions with different preprocessing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, normal, and pneumonia from X-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

COVID-19 大流行病对整个世界造成了严重破坏，夺走了 50 多万人的生命，并以前所未有的规模颠覆了世界经济。随着世界争先恐后地寻找可能的疫苗，及早发现和遏制是唯一的补救措施。现有的高精度诊断技术（如 RT-PCR）既昂贵又复杂，需要熟练的人员进行标本采集和筛查，从而导致覆盖率较低。因此，排除直接人工干预的方法备受追捧，而人工智能驱动的自动诊断，尤其是射线照相图像，引起了研究人员的兴趣。这项调查标志着对迄今为止完成的基于深度学习的 COVID-19 自动检测工作的详细检查，对可用数据集的比较，有条不紊的挑战，如不平衡数据集等，以及采用不同预处理方法的可能解决方案，以及该领域未来探索的范围。我们还对 315 个深度模型在诊断 COVID-19、正常和肺炎方面的性能进行了基准测试，这些模型是从其他四个模型创建的自定义数据集的 X 射线图像中诊断出来的。该数据集可在 https://github.com/rgbnihal2/COVID-19-X-ray-Dataset 上公开获得。我们的结果表明，具有二次 SVM 分类器的 DenseNet201 模型表现最好（准确度：98.16%，灵敏度：98.93%，特异性：98.77%），并且在其他类似架构中也保持高精度。这证明，即使放射线照相图像对放射科医生来说可能不是决定性的，但对于检测 COVID-19 的深度学习算法来说却是如此。