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10.1016/j.irbm.2020.05.003 http://scihub22266oqcxt.onion/10.1016/j.irbm.2020.05.003 C7238986!7238986 !32837678     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32837678 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Ing+Rech+Biomed 2022 ; 43 (2 ): 87-92 Nephropedia Template TP {{tp|p=32837678 |t=2022. Deep Transfer Learning Based Classification Model for COVID-19 Disease |pdf=|usr=}}{{32837678 }} gab.com Text Deep Transfer Learning Based Classification Model for COVID-19 Disease JO: Ing Rech Biomed http://www.kidney.de/pget.php?&tw=1&pmid=32837678 #FOAvip The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography (CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the bilateral change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models. Twit Text FOAVip Deep Transfer Learning Based Classification Model for COVID-19 Disease ????Ing Rech Biomed http://www.kidney.de/pget.php?&tw=1&pmid=32837678 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32837678 #FOAvip English Wikipedia <ref>{{Cite pmid|32837678 }}</ref> Deep Transfer Learning Based Classification Model for COVID-19 Disease #MMPMID32837678 Pathak Y ; Shukla PK ; Tiwari A ; Stalin S ; Singh S ; Shukla PK Ing Rech Biomed 2022[Apr]; 43 (2 ): 87-92 PMID32837678 show ga The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography (CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the bilateral change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models. ?Deep Transfer Learning Based Classification Model for COVID-19 Disease(epmc).pdf DeepDyve Pubget Overpricing