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10.1007/s00138-020-01128-8 http://scihub22266oqcxt.onion/10.1007/s00138-020-01128-8 33169050!7609373!33169050     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Mach+Vis+Appl 2021 ; 32 (1): 14 Nephropedia Template TP {{tp|p=33169050|t=2021. A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis |pdf=|usr=}}{{33169050}} gab.com Text A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis JO: Mach Vis Appl http://www.kidney.de/pget.php?&tw=1&pmid=33169050 #FOAvip 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. Twit Text FOAVip A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis ????Mach Vis Appl http://www.kidney.de/pget.php?&tw=1&pmid=33169050 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33169050 #FOAvip English Wikipedia <ref>{{Cite pmid|33169050}}</ref> A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis #MMPMID33169050 Zhang YD; Satapathy SC; Liu S; Li GR Mach Vis Appl 2021[]; 32 (1): 14 PMID33169050show ga 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. äA five-layer deep convolutional neural network with stochastic pooling(epmc).pdf DeepDyve Pubget Overpricing