Zhang Yu-Dong, Satapathy Suresh Chandra, Liu Shuaiqi, Li Guang-Run
School of Informatics, University of Leicester, Leicester, LE1 7RH UK.
Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589 Saudi Arabia.
Mach Vis Appl. 2021;32(1):14. doi: 10.1007/s00138-020-01128-8. Epub 2020 Nov 3.
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，新型冠状病毒肺炎已在227多个国家和地区以及26艘海军舰艇上造成2159万例确诊病例。胸部CT是检测新型冠状病毒肺炎的有效方法。本研究提出了一种新型深度学习模型，该模型可以更准确、快速地在胸部CT上诊断新型冠状病毒肺炎。基于传统深度卷积神经网络（DCNN）模型，我们提出了三点改进：（i）引入随机池化以取代平均池化和最大池化；（ii）将卷积层与批量归一化层相结合，得到卷积块（CB）；（iii）将随机失活层与全连接层相结合，得到全连接块（FCB）。在从正常受试者中识别新型冠状病毒肺炎方面，我们的算法灵敏度为93.28%±1.50%，特异性为94.00%±1.56%，准确率为93.64%±1.42%。我们证明使用随机池化比平均池化和最大池化具有更好的性能。我们比较了不同的结构配置，并证明我们的3CB + 2FCB具有最佳性能。所提出的模型在基于胸部CT图像检测新型冠状病毒肺炎方面是有效的。
