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10.1155/2021/5527923 http://scihub22266oqcxt.onion/10.1155/2021/5527923 33936188!8042993!33936188     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Intell+Neurosci 2021 ; 2021 (ä): 5527923 Nephropedia Template TP {{tp|p=33936188|t=2021. Detection of COVID-19 from CT Lung Scans Using Transfer Learning |pdf=|usr=}}{{33936188}} gab.com Text Detection of COVID-19 from CT Lung Scans Using Transfer Learning JO: Comput Intell Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=33936188 #FOAvip This paper aims to investigate the use of transfer learning architectures in the detection of COVID-19 from CT lung scans. The study evaluates the performances of various transfer learning architectures, as well as the effects of the standard Histogram Equalization and Contrast Limited Adaptive Histogram Equalization. The findings of this study suggest that transfer learning-based frameworks are an alternative to the contemporary methods used to detect the presence of the virus in patients. The highest performing model, the VGG-19 implemented with the Contrast Limited Adaptive Histogram Equalization, on a SARS-CoV-2 dataset, achieved an accuracy and recall of 95.75% and 97.13%, respectively. Twit Text FOAVip Detection of COVID-19 from CT Lung Scans Using Transfer Learning ????Comput Intell Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=33936188 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33936188 #FOAvip English Wikipedia <ref>{{Cite pmid|33936188}}</ref> Detection of COVID-19 from CT Lung Scans Using Transfer Learning #MMPMID33936188 Lawton S; Viriri S Comput Intell Neurosci 2021[]; 2021 (ä): 5527923 PMID33936188show ga This paper aims to investigate the use of transfer learning architectures in the detection of COVID-19 from CT lung scans. The study evaluates the performances of various transfer learning architectures, as well as the effects of the standard Histogram Equalization and Contrast Limited Adaptive Histogram Equalization. The findings of this study suggest that transfer learning-based frameworks are an alternative to the contemporary methods used to detect the presence of the virus in patients. The highest performing model, the VGG-19 implemented with the Contrast Limited Adaptive Histogram Equalization, on a SARS-CoV-2 dataset, achieved an accuracy and recall of 95.75% and 97.13%, respectively. |*Machine Learning[MESH] |*SARS-CoV-2[MESH] |Area Under Curve[MESH] |COVID-19/*diagnostic imaging[MESH] |Datasets as Topic[MESH] |Diagnosis, Computer-Assisted[MESH] |Humans[MESH] |Image Processing, Computer-Assisted/*methods[MESH] |Lung/*diagnostic imaging/virology[MESH] |ROC Curve[MESH]Detection of COVID-19 from CT Lung Scans Using Transfer Learning (epmc).pdf DeepDyve Pubget Overpricing