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10.1002/mp.15044 http://scihub22266oqcxt.onion/10.1002/mp.15044 34117783!8420535!34117783     free     free     free 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 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Med+Phys 2021 ; 48 (8): 4334-4349 Nephropedia Template TP {{tp|p=34117783|t=2021. COVID-19 diagnosis on CT scan images using a generative adversarial network and concatenated feature pyramid network with an attention mechanism |pdf=|usr=}}{{34117783}} gab.com Text COVID-19 diagnosis on CT scan images using a generative adversarial network and concatenated feature pyramid network with an attention mechanism JO: Med Phys http://www.kidney.de/pget.php?&tw=1&pmid=34117783 #FOAvip OBJECTIVE: Coronavirus disease 2019 (COVID-19) has caused hundreds of thousands of infections and deaths. Efficient diagnostic methods could help curb its global spread. The purpose of this study was to develop and evaluate a method for accurately diagnosing COVID-19 based on computed tomography (CT) scans in real time. METHODS: We propose an architecture named "concatenated feature pyramid network" ("Concat-FPN") with an attention mechanism, by concatenating feature maps of multiple. The proposed architecture is then used to form two networks, which we call COVID-CT-GAN and COVID-CT-DenseNet, the former for data augmentation and the latter for data classification. RESULTS: The proposed method is evaluated on 3 different numbers of magnitude of COVID-19 CT datasets. Compared with the method without GANs for data augmentation or the original network auxiliary classifier generative adversarial network, COVID-CT-GAN increases the accuracy by 2% to 3%, the recall by 2% to 4%, the precision by 1% to 3%, the F1-score by 1% to 3%, and the area under the curve by 1% to 4%. Compared with the original network DenseNet-201, COVID-CT-DenseNet increases the accuracy by 1% to 3%, the recall by 4% to 9%, the precision by 1%, the F1-score by 1% to 3%, and the area under the curve by 2%. CONCLUSION: The experimental results show that our method improves the efficiency of diagnosing COVID-19 on CT images, and helps overcome the problem of limited training data when using deep learning methods to diagnose COVID-19. SIGNIFICANCE: Our method can help clinicians build deep learning models using their private datasets to achieve automatic diagnosis of COVID-19 with a high precision. Twit Text FOAVip COVID-19 diagnosis on CT scan images using a generative adversarial network and concatenated feature pyramid network with an attention mechanism ????Med Phys http://www.kidney.de/pget.php?&tw=1&pmid=34117783 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34117783 #FOAvip English Wikipedia <ref>{{Cite pmid|34117783}}</ref> COVID-19 diagnosis on CT scan images using a generative adversarial network and concatenated feature pyramid network with an attention mechanism #MMPMID34117783 Li Z; Zhang J; Li B; Gu X; Luo X Med Phys 2021[Aug]; 48 (8): 4334-4349 PMID34117783show ga OBJECTIVE: Coronavirus disease 2019 (COVID-19) has caused hundreds of thousands of infections and deaths. Efficient diagnostic methods could help curb its global spread. The purpose of this study was to develop and evaluate a method for accurately diagnosing COVID-19 based on computed tomography (CT) scans in real time. METHODS: We propose an architecture named "concatenated feature pyramid network" ("Concat-FPN") with an attention mechanism, by concatenating feature maps of multiple. The proposed architecture is then used to form two networks, which we call COVID-CT-GAN and COVID-CT-DenseNet, the former for data augmentation and the latter for data classification. RESULTS: The proposed method is evaluated on 3 different numbers of magnitude of COVID-19 CT datasets. Compared with the method without GANs for data augmentation or the original network auxiliary classifier generative adversarial network, COVID-CT-GAN increases the accuracy by 2% to 3%, the recall by 2% to 4%, the precision by 1% to 3%, the F1-score by 1% to 3%, and the area under the curve by 1% to 4%. Compared with the original network DenseNet-201, COVID-CT-DenseNet increases the accuracy by 1% to 3%, the recall by 4% to 9%, the precision by 1%, the F1-score by 1% to 3%, and the area under the curve by 2%. CONCLUSION: The experimental results show that our method improves the efficiency of diagnosing COVID-19 on CT images, and helps overcome the problem of limited training data when using deep learning methods to diagnose COVID-19. SIGNIFICANCE: Our method can help clinicians build deep learning models using their private datasets to achieve automatic diagnosis of COVID-19 with a high precision. |*COVID-19[MESH] |COVID-19 Testing[MESH] |Humans[MESH] |Radionuclide Imaging[MESH] |SARS-CoV-2[MESH]COVID-19 diagnosis on CT scan images using a generative adversarial ne(epmc).pdf DeepDyve Pubget Overpricing