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10.1080/07391102.2020.1788642 http://scihub22266oqcxt.onion/10.1080/07391102.2020.1788642 32619398!ä!32619398 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       J+Biomol+Struct+Dyn 2021 ; 39 (15): 5682-5689 Nephropedia Template TP {{tp|p=32619398|t=2021. Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning |pdf=|usr=}}{{32619398}} gab.com Text Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning JO: J Biomol Struct Dyn http://www.kidney.de/pget.php?&tw=1&pmid=32619398 #FOAvip Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or COVID (-). The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches.Communicated by Ramaswamy H. Sarma. Twit Text FOAVip Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning ????J Biomol Struct Dyn http://www.kidney.de/pget.php?&tw=1&pmid=32619398 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32619398 #FOAvip English Wikipedia <ref>{{Cite pmid|32619398}}</ref> Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning #MMPMID32619398 Jaiswal A; Gianchandani N; Singh D; Kumar V; Kaur M J Biomol Struct Dyn 2021[Sep]; 39 (15): 5682-5689 PMID32619398show ga Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or COVID (-). The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches.Communicated by Ramaswamy H. Sarma. |*COVID-19[MESH] |*Deep Learning[MESH] |Humans[MESH] |Neural Networks, Computer[MESH] |SARS-CoV-2[MESH]Classification of the COVID-19 infected patients using DenseNet201 bas(epmc).pdf DeepDyve Pubget Overpricing