A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis

• Published in: Machine vision and applications Vol. 32; no. 1; p. 14
• Main Authors: Zhang, Yu-Dong, Satapathy, Suresh Chandra, Liu, Shuaiqi, Li, Guang-Run
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Chest; Communications Engineering; Computed tomography; Computer Science; Coronaviruses; COVID-19; Image Processing and Computer Vision; Machine learning; Naval vessels; Networks; Neural networks; Pattern Recognition; Special Issue on Deep Learning Methods for Biomedical Information Analysis; Special Issue Paper; Vision systems; COVID-19; Stochastic pooling; Deep convolutional neural network; Convolution block; Batch normalization; Dropout; Fully connected block
• ISSN: 0932-8092; 1432-1769
• DOI: 10.1007/s00138-020-01128-8

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.