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10.1016/j.compbiomed.2021.104575 http://scihub22266oqcxt.onion/10.1016/j.compbiomed.2021.104575 34153789!8196483!34153789     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34153789&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 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Biol+Med 2021 ; 135 (ä): 104575 Nephropedia Template TP {{tp|p=34153789|t=2021. Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan |pdf=|usr=}}{{34153789}} gab.com Text Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan JO: Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=34153789 #FOAvip This research work aims to identify COVID-19 through deep learning models using lung CT-SCAN images. In order to enhance lung CT scan efficiency, a super-residual dense neural network was applied. The experimentation has been carried out using benchmark datasets like SARS-COV-2 CT-Scan and Covid-CT Scan. To mark COVID-19 as positive or negative for the improved CT scan, existing pre-trained models such as XceptionNet, MobileNet, InceptionV3, DenseNet, ResNet50, and VGG (Visual Geometry Group)16 have been used. Taking CT scans with super resolution using a residual dense neural network in the pre-processing step resulted in improving the accuracy, F1 score, precision, and recall of the proposed model. On the dataset Covid-CT Scan and SARS-COV-2 CT-Scan, the MobileNet model provided a precision of 94.12% and 100% respectively. Twit Text FOAVip Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan ????Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=34153789 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34153789 #FOAvip English Wikipedia <ref>{{Cite pmid|34153789}}</ref> Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan #MMPMID34153789 Arora V; Ng EY; Leekha RS; Darshan M; Singh A Comput Biol Med 2021[Aug]; 135 (ä): 104575 PMID34153789show ga This research work aims to identify COVID-19 through deep learning models using lung CT-SCAN images. In order to enhance lung CT scan efficiency, a super-residual dense neural network was applied. The experimentation has been carried out using benchmark datasets like SARS-COV-2 CT-Scan and Covid-CT Scan. To mark COVID-19 as positive or negative for the improved CT scan, existing pre-trained models such as XceptionNet, MobileNet, InceptionV3, DenseNet, ResNet50, and VGG (Visual Geometry Group)16 have been used. Taking CT scans with super resolution using a residual dense neural network in the pre-processing step resulted in improving the accuracy, F1 score, precision, and recall of the proposed model. On the dataset Covid-CT Scan and SARS-COV-2 CT-Scan, the MobileNet model provided a precision of 94.12% and 100% respectively. |*COVID-19/diagnostic imaging[MESH] |*Deep Learning[MESH] |*Lung/diagnostic imaging[MESH] |Humans[MESH]Transfer learning-based approach for detecting COVID-19 ailment in lun(epmc).pdf DeepDyve Pubget Overpricing