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?:abstract
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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
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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 © 2020, Springer-Verlag GmbH Germany, part of Springer Nature
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