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

Yu Dong Zhang, Suresh Chandra Satapathy, Shuaiqi Liu, Guang Run Li

Research output: Contribution to journalArticlepeer-review

Abstract

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. 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.
Original languageEnglish
JournalMachine Vision and Applications
Volume32
Issue number1
DOIs
StatePublished - 1 Feb 2021

Fingerprint Dive into the research topics of 'A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis'. Together they form a unique fingerprint.

Cite this