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10.1016/j.imu.2021.100620 http://scihub22266oqcxt.onion/10.1016/j.imu.2021.100620 34075340!8158400!34075340     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       Inform+Med+Unlocked 2021 ; 24 (ä): 100620 Nephropedia Template TP {{tp|p=34075340|t=2021. COV-SNET: A deep learning model for X-ray-based COVID-19 classification |pdf=|usr=}}{{34075340}} gab.com Text COV-SNET: A deep learning model for X-ray-based COVID-19 classification JO: Inform Med Unlocked http://www.kidney.de/pget.php?&tw=1&pmid=34075340 #FOAvip The AI research community has recently been intensely focused on diagnosing COVID-19 by applying deep learning technology to the X-ray scans taken of COVID-19 patients. Differentiating COVID-19 from other pneumonia-inducing illnesses is a highly challenging task as it shares many of the same imaging characteristics as other pulmonary diseases. This is especially true given the small number of COVID-19 X-rays that are publicly available. Deep learning experts commonly use transfer learning to offset the small number of images typically available in medical imaging tasks. Our COV-SNET model is a deep neural network that was pretrained on over one hundred thousand X-ray images. In this paper, we designed two COV-SNET models with the purpose of diagnosing COVID-19. The experimental results demonstrate the robustness of our deep learning models, ultimately achieving sensitivities of 95% for our three-class and two-class models. We also discuss the strengths and weaknesses of such an approach, focusing mainly on the limitations of public X-ray datasets on current COVID-19 deep learning models. Finally, we conclude with possible future directions for this research. Twit Text FOAVip COV-SNET: A deep learning model for X-ray-based COVID-19 classification ????Inform Med Unlocked http://www.kidney.de/pget.php?&tw=1&pmid=34075340 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34075340 #FOAvip English Wikipedia <ref>{{Cite pmid|34075340}}</ref> COV-SNET: A deep learning model for X-ray-based COVID-19 classification #MMPMID34075340 Hertel R; Benlamri R Inform Med Unlocked 2021[]; 24 (ä): 100620 PMID34075340show ga The AI research community has recently been intensely focused on diagnosing COVID-19 by applying deep learning technology to the X-ray scans taken of COVID-19 patients. Differentiating COVID-19 from other pneumonia-inducing illnesses is a highly challenging task as it shares many of the same imaging characteristics as other pulmonary diseases. This is especially true given the small number of COVID-19 X-rays that are publicly available. Deep learning experts commonly use transfer learning to offset the small number of images typically available in medical imaging tasks. Our COV-SNET model is a deep neural network that was pretrained on over one hundred thousand X-ray images. In this paper, we designed two COV-SNET models with the purpose of diagnosing COVID-19. The experimental results demonstrate the robustness of our deep learning models, ultimately achieving sensitivities of 95% for our three-class and two-class models. We also discuss the strengths and weaknesses of such an approach, focusing mainly on the limitations of public X-ray datasets on current COVID-19 deep learning models. Finally, we conclude with possible future directions for this research. äCOV-SNET: A deep learning model for X-ray-based COVID-19 classificatio(epmc).pdf DeepDyve Pubget Overpricing