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10.1155/2021/5527271 http://scihub22266oqcxt.onion/10.1155/2021/5527271 34055034!8112196!34055034     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Math+Methods+Med 2021 ; 2021 (ä): 5527271 Nephropedia Template TP {{tp|p=34055034|t=2021. Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19 |pdf=|usr=}}{{34055034}} gab.com Text Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19 JO: Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=34055034 #FOAvip The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs. In this paper, we investigate the application of pre-trained models in diagnosing patients who are positive for COVID-19 and differentiating it from normal patients, who tested negative for coronavirus. The study aims to compare the generalization capabilities of deep learning models with two thoracic radiologists in diagnosing COVID-19 chest CT images. A dataset of 3000 images was obtained from the Near East Hospital, Cyprus, and used to train and to test the three employed pre-trained models. In a test set of 250 images used to evaluate the deep neural networks and the radiologists, it was found that deep networks (ResNet-18, ResNet-50, and DenseNet-201) can outperform the radiologists in terms of higher accuracy (97.8%), sensitivity (98.1%), specificity (97.3%), precision (98.4%), and F1-score (198.25%), in classifying COVID-19 images. Twit Text FOAVip Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19 ????Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=34055034 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34055034 #FOAvip English Wikipedia <ref>{{Cite pmid|34055034}}</ref> Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19 #MMPMID34055034 Helwan A; Ma'aitah MKS; Hamdan H; Ozsahin DU; Tuncyurek O Comput Math Methods Med 2021[]; 2021 (ä): 5527271 PMID34055034show ga The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs. In this paper, we investigate the application of pre-trained models in diagnosing patients who are positive for COVID-19 and differentiating it from normal patients, who tested negative for coronavirus. The study aims to compare the generalization capabilities of deep learning models with two thoracic radiologists in diagnosing COVID-19 chest CT images. A dataset of 3000 images was obtained from the Near East Hospital, Cyprus, and used to train and to test the three employed pre-trained models. In a test set of 250 images used to evaluate the deep neural networks and the radiologists, it was found that deep networks (ResNet-18, ResNet-50, and DenseNet-201) can outperform the radiologists in terms of higher accuracy (97.8%), sensitivity (98.1%), specificity (97.3%), precision (98.4%), and F1-score (198.25%), in classifying COVID-19 images. |*Deep Learning[MESH] |*Radiologists/statistics & numerical data[MESH] |*SARS-CoV-2[MESH] |*Tomography, X-Ray Computed/statistics & numerical data[MESH] |COVID-19 Testing/*methods/statistics & numerical data[MESH] |COVID-19/*diagnostic imaging/epidemiology[MESH] |Databases, Factual[MESH] |Diagnosis, Computer-Assisted/methods/statistics & numerical data[MESH] |Diagnostic Errors/statistics & numerical data[MESH] |Expert Testimony/statistics & numerical data[MESH] |Humans[MESH] |Lung/diagnostic imaging[MESH] |Mathematical Concepts[MESH] |Neural Networks, Computer[MESH]Radiologists versus Deep Convolutional Neural Networks: A Comparative (epmc).pdf DeepDyve Pubget Overpricing