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10.1016/j.csbj.2021.02.016 http://scihub22266oqcxt.onion/10.1016/j.csbj.2021.02.016 33680351!7923948!33680351     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Struct+Biotechnol+J 2021 ; 19 (ä): 1391-1399 Nephropedia Template TP {{tp|p=33680351|t=2021. Deep learning for COVID-19 chest CT (computed tomography) image analysis: A lesson from lung cancer |pdf=|usr=}}{{33680351}} gab.com Text Deep learning for COVID-19 chest CT (computed tomography) image analysis: A lesson from lung cancer JO: Comput Struct Biotechnol J http://www.kidney.de/pget.php?&tw=1&pmid=33680351 #FOAvip As a recent global health emergency, the quick and reliable diagnosis of COVID-19 is urgently needed. Thus, many artificial intelligence (AI)-base methods are proposed for COVID-19 chest CT (computed tomography) image analysis. However, there are very limited COVID-19 chest CT images publicly available to evaluate those deep neural networks. On the other hand, a huge amount of CT images from lung cancer are publicly available. To build a reliable deep learning model trained and tested with a larger scale dataset, the proposed model builds a public COVID-19 CT dataset, containing 1186 CT images synthesized from lung cancer CT images using CycleGAN. Additionally, various deep learning models are tested with synthesized or real chest CT images for COVID-19 and Non-COVID-19 classification. In comparison, all models achieve excellent results in accuracy, precision, recall and F1 score for both synthesized and real COVID-19 CT images, demonstrating the reliable of the synthesized dataset. The public dataset and deep learning models can facilitate the development of accurate and efficient diagnostic testing for COVID-19. Twit Text FOAVip Deep learning for COVID-19 chest CT (computed tomography) image analysis: A lesson from lung cancer ????Comput Struct Biotechnol J http://www.kidney.de/pget.php?&tw=1&pmid=33680351 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33680351 #FOAvip English Wikipedia <ref>{{Cite pmid|33680351}}</ref> Deep learning for COVID-19 chest CT (computed tomography) image analysis: A lesson from lung cancer #MMPMID33680351 Jiang H; Tang S; Liu W; Zhang Y Comput Struct Biotechnol J 2021[]; 19 (ä): 1391-1399 PMID33680351show ga As a recent global health emergency, the quick and reliable diagnosis of COVID-19 is urgently needed. Thus, many artificial intelligence (AI)-base methods are proposed for COVID-19 chest CT (computed tomography) image analysis. However, there are very limited COVID-19 chest CT images publicly available to evaluate those deep neural networks. On the other hand, a huge amount of CT images from lung cancer are publicly available. To build a reliable deep learning model trained and tested with a larger scale dataset, the proposed model builds a public COVID-19 CT dataset, containing 1186 CT images synthesized from lung cancer CT images using CycleGAN. Additionally, various deep learning models are tested with synthesized or real chest CT images for COVID-19 and Non-COVID-19 classification. In comparison, all models achieve excellent results in accuracy, precision, recall and F1 score for both synthesized and real COVID-19 CT images, demonstrating the reliable of the synthesized dataset. The public dataset and deep learning models can facilitate the development of accurate and efficient diagnostic testing for COVID-19. äDeep learning for COVID-19 chest CT (computed tomography) image analys(epmc).pdf DeepDyve Pubget Overpricing