A neural network is one of the current trends in deep learning, which is increasingly gaining attention owing to its contribution in transforming the different facets of human life. It also paves a way to approach the current crisis caused by the coronavirus disease (COVID-19) from all scientific directions. Convolutional neural network (CNN), a type of neural network, is extensively applied in the medical field, and is particularly useful in the current COVID-19 pandemic. In this article, we present the application of CNNs for the diagnosis and prognosis of COVID-19 using X-ray and computed tomography (CT) images of COVID-19 patients. The CNN models discussed in this review were mainly developed for the detection, classification, and segmentation of COVID-19 images. The base models used for detection and classification were AlexNet, Visual Geometry Group Network with 16 layers, residual network, DensNet, GoogLeNet, MobileNet, Inception, and extreme Inception. U-Net and voxel-based broad learning network were used for segmentation. Even with limited datasets, these methods proved to be beneficial for efficiently identifying the occurrence of COVID-19. To further validate these observations, we conducted an experimental study using a simple CNN framework for the binary classification of COVID-19 CT images. We achieved an accuracy of 93% with an F1-score of 0.93. Thus, with the availability of improved medical image datasets, it is evident that CNNs are very useful for the efficient diagnosis and prognosis of COVID-19.

中文翻译：

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用于冠状病毒疾病大流行的诊断和预后的卷积神经网络


神经网络是当前深度学习的趋势之一，由于其在改变人类生活的不同方面的贡献而日益受到关注。它还为从各个科学方向应对当前由冠状病毒疾病（COVID-19）引起的危机铺平了道路。卷积神经网络（CNN）是神经网络的一种，在医学领域有着广泛的应用，在当前的COVID-19大流行中尤其有用。在本文中，我们利用 COVID-19 患者的 X 射线和计算机断层扫描 (CT) 图像介绍了 CNN 在 COVID-19 诊断和预后中的应用。本综述中讨论的 CNN 模型主要是为 COVID-19 图像的检测、分类和分割而开发的。用于检测和分类的基础模型是AlexNet、16层视觉几何组网络、残差网络、DensNet、GoogLeNet、MobileNet、Inception和Extreme Inception。 U-Net 和基于体素的广泛学习网络用于分割。即使数据集有限，这些方法也被证明有助于有效识别 COVID-19 的发生。为了进一步验证这些观察结果，我们使用简单的 CNN 框架对 COVID-19 CT 图像进行二元分类进行了实验研究。我们的准确率达到 93%，F1 分数为 0.93。因此，随着改进的医学图像数据集的出现，CNN 对于 COVID-19 的有效诊断和预后非常有用。