Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect COVID-19. This study proposed a novel deep learning model that can diagnose COVID-19 on chest CT more accurately and swiftly. Based on traditional deep convolutional neural network (DCNN) model, we proposed three improvements: (i) We introduced stochastic pooling to replace average pooling and max pooling; (ii) We combined conv layer with batch normalization layer and obtained the conv block (CB); (iii) We combined dropout layer with fully connected layer and obtained the fully connected block (FCB). Our algorithm achieved a sensitivity of 93.28% ± 1.50%, a specificity of 94.00% ± 1.56%, and an accuracy of 93.64% ± 1.42%, in identifying COVID-19 from normal subjects. We proved using stochastic pooling yields better performance than average pooling and max pooling. We compared different structure configurations and proved our 3CB + 2FCB yields the best performance. The proposed model is effective in detecting COVID-19 based on chest CT images.

截至2020年8月17日，COVID-19已在227多个国家和地区和26艘军舰上造成2159万例确诊病例。胸部CT是检测COVID-19的有效方法。这项研究提出了一种新颖的深度学习模型，该模型可以更准确，快速地诊断胸部CT上的COVID-19。在传统的深度卷积神经网络（DCNN）模型的基础上，我们提出了三点改进：（i）引入了随机池来代替平均池和最大池；（ii）将conv层与批处理归一化层合并，获得conv块（CB）；（iii）我们将辍学层与全连接层组合在一起，获得了全连接块（FCB）。在从正常受试者中识别COVID-19时，我们的算法实现了93.28％±1.50％的灵敏度，94.00％±1.56％的特异性和93.64％±1.42％的准确性。我们证明，使用随机池比平均池和最大池具有更好的性能。我们比较了不同的结构配置，并证明了我们的3CB + 2FCB具有最佳性能。该模型可有效地基于胸部CT图像检测COVID-19。