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10.1007/s10140-020-01886-y http://scihub22266oqcxt.onion/10.1007/s10140-020-01886-y 33523309!7848247!33523309     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33523309&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 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       Emerg+Radiol 2021 ; 28 (3): 497-505 Nephropedia Template TP {{tp|p=33523309|t=2021. Diagnosis of COVID-19 using CT scan images and deep learning techniques |pdf=|usr=}}{{33523309}} gab.com Text Diagnosis of COVID-19 using CT scan images and deep learning techniques JO: Emerg Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33523309 #FOAvip Early diagnosis of the coronavirus disease in 2019 (COVID-19) is essential for controlling this pandemic. COVID-19 has been spreading rapidly all over the world. There is no vaccine available for this virus yet. Fast and accurate COVID-19 screening is possible using computed tomography (CT) scan images. The deep learning techniques used in the proposed method is based on a convolutional neural network (CNN). Our manuscript focuses on differentiating the CT scan images of COVID-19 and non-COVID 19 CT using different deep learning techniques. A self-developed model named CTnet-10 was designed for the COVID-19 diagnosis, having an accuracy of 82.1%. Also, other models that we tested are DenseNet-169, VGG-16, ResNet-50, InceptionV3, and VGG-19. The VGG-19 proved to be superior with an accuracy of 94.52% as compared to all other deep learning models. Automated diagnosis of COVID-19 from the CT scan pictures can be used by the doctors as a quick and efficient method for COVID-19 screening. Twit Text FOAVip Diagnosis of COVID-19 using CT scan images and deep learning techniques ????Emerg Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33523309 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33523309 #FOAvip English Wikipedia <ref>{{Cite pmid|33523309}}</ref> Diagnosis of COVID-19 using CT scan images and deep learning techniques #MMPMID33523309 Shah V; Keniya R; Shridharani A; Punjabi M; Shah J; Mehendale N Emerg Radiol 2021[Jun]; 28 (3): 497-505 PMID33523309show ga Early diagnosis of the coronavirus disease in 2019 (COVID-19) is essential for controlling this pandemic. COVID-19 has been spreading rapidly all over the world. There is no vaccine available for this virus yet. Fast and accurate COVID-19 screening is possible using computed tomography (CT) scan images. The deep learning techniques used in the proposed method is based on a convolutional neural network (CNN). Our manuscript focuses on differentiating the CT scan images of COVID-19 and non-COVID 19 CT using different deep learning techniques. A self-developed model named CTnet-10 was designed for the COVID-19 diagnosis, having an accuracy of 82.1%. Also, other models that we tested are DenseNet-169, VGG-16, ResNet-50, InceptionV3, and VGG-19. The VGG-19 proved to be superior with an accuracy of 94.52% as compared to all other deep learning models. Automated diagnosis of COVID-19 from the CT scan pictures can be used by the doctors as a quick and efficient method for COVID-19 screening. |*Deep Learning[MESH] |COVID-19/*diagnostic imaging[MESH] |Diagnosis, Differential[MESH] |Early Diagnosis[MESH] |Humans[MESH] |Pandemics[MESH] |SARS-CoV-2[MESH]Diagnosis of COVID-19 using CT scan images and deep learning technique(epmc).pdf DeepDyve Pubget Overpricing