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10.1007/s12652-020-02669-6 http://scihub22266oqcxt.onion/10.1007/s12652-020-02669-6 33224307!7667280!33224307     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       J+Ambient+Intell+Humaniz+Comput 2023 ; 14 (5): 5541-5553 Nephropedia Template TP {{tp|p=33224307|t=2023. Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images |pdf=|usr=}}{{33224307}} gab.com Text Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images JO: J Ambient Intell Humaniz Comput http://www.kidney.de/pget.php?&tw=1&pmid=33224307 #FOAvip The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes novel coronavirus disease (COVID-19) outbreak in more than 200 countries around the world. The early diagnosis of infected patients is needed to discontinue this outbreak. The diagnosis of coronavirus infection from radiography images is the fastest method. In this paper, two different ensemble deep transfer learning models have been designed for COVID-19 diagnosis utilizing the chest X-rays. Both models have utilized pre-trained models for better performance. They are able to differentiate COVID-19, viral pneumonia, and bacterial pneumonia. Both models have been developed to improve the generalization capability of the classifier for binary and multi-class problems. The proposed models have been tested on two well-known datasets. Experimental results reveal that the proposed framework outperforms the existing techniques in terms of sensitivity, specificity, and accuracy. Twit Text FOAVip Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images ????J Ambient Intell Humaniz Comput http://www.kidney.de/pget.php?&tw=1&pmid=33224307 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33224307 #FOAvip English Wikipedia <ref>{{Cite pmid|33224307}}</ref> Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images #MMPMID33224307 Gianchandani N; Jaiswal A; Singh D; Kumar V; Kaur M J Ambient Intell Humaniz Comput 2023[]; 14 (5): 5541-5553 PMID33224307show ga The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes novel coronavirus disease (COVID-19) outbreak in more than 200 countries around the world. The early diagnosis of infected patients is needed to discontinue this outbreak. The diagnosis of coronavirus infection from radiography images is the fastest method. In this paper, two different ensemble deep transfer learning models have been designed for COVID-19 diagnosis utilizing the chest X-rays. Both models have utilized pre-trained models for better performance. They are able to differentiate COVID-19, viral pneumonia, and bacterial pneumonia. Both models have been developed to improve the generalization capability of the classifier for binary and multi-class problems. The proposed models have been tested on two well-known datasets. Experimental results reveal that the proposed framework outperforms the existing techniques in terms of sensitivity, specificity, and accuracy. äRapid COVID-19 diagnosis using ensemble deep transfer learning models (epmc).pdf DeepDyve Pubget Overpricing