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10.1007/s12652-021-03306-6 http://scihub22266oqcxt.onion/10.1007/s12652-021-03306-6 34025813!8123104!34025813     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Ambient+Intell+Humaniz+Comput 2023 ; 14 (1): 469-478 Nephropedia Template TP {{tp|p=34025813|t=2023. Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images |pdf=|usr=}}{{34025813}} gab.com Text Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images JO: J Ambient Intell Humaniz Comput http://www.kidney.de/pget.php?&tw=1&pmid=34025813 #FOAvip Around the world, more than 250 countries are affected by the COVID-19 pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This outbreak can be controlled only by the diagnosis of the COVID-19 infection in early stages. It is found that the radiographic images are ideal for the fastest diagnosis of COVID-19 infection. This paper proposes an ensemble model which detects the COVID-19 infection in the early stage with the use of chest X-ray images. The transfer learning enables to reuse the pretrained models. The ensemble learning integrates various transfer learning models, i.e., EfficientNet, GoogLeNet, and XceptionNet, to design the proposed model. These models can categorize patients as COVID-19 (+), pneumonia (+), tuberculosis (+), or healthy. The proposed model enhances the classifier's generalization ability for both binary and multiclass COVID-19 datasets. Two popular datasets are used to evaluate the performance of the proposed ensemble model. The comparative analysis validates that the proposed model outperforms the state-of-art models in terms of various performance metrics. Twit Text FOAVip Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images ????J Ambient Intell Humaniz Comput http://www.kidney.de/pget.php?&tw=1&pmid=34025813 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34025813 #FOAvip English Wikipedia <ref>{{Cite pmid|34025813}}</ref> Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images #MMPMID34025813 Kumar N; Gupta M; Gupta D; Tiwari S J Ambient Intell Humaniz Comput 2023[]; 14 (1): 469-478 PMID34025813show ga Around the world, more than 250 countries are affected by the COVID-19 pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This outbreak can be controlled only by the diagnosis of the COVID-19 infection in early stages. It is found that the radiographic images are ideal for the fastest diagnosis of COVID-19 infection. This paper proposes an ensemble model which detects the COVID-19 infection in the early stage with the use of chest X-ray images. The transfer learning enables to reuse the pretrained models. The ensemble learning integrates various transfer learning models, i.e., EfficientNet, GoogLeNet, and XceptionNet, to design the proposed model. These models can categorize patients as COVID-19 (+), pneumonia (+), tuberculosis (+), or healthy. The proposed model enhances the classifier's generalization ability for both binary and multiclass COVID-19 datasets. Two popular datasets are used to evaluate the performance of the proposed ensemble model. The comparative analysis validates that the proposed model outperforms the state-of-art models in terms of various performance metrics. äNovel deep transfer learning model for COVID-19 patient detection usin(epmc).pdf DeepDyve Pubget Overpricing