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10.1016/j.compbiomed.2021.104375 http://scihub22266oqcxt.onion/10.1016/j.compbiomed.2021.104375 33866253!8048393!33866253     free     free     free 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 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 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 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Biol+Med 2021 ; 133 (ä): 104375 Nephropedia Template TP {{tp|p=33866253|t=2021. CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR |pdf=|usr=}}{{33866253}} gab.com Text CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR JO: Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33866253 #FOAvip To mitigate the spread of the current coronavirus disease 2019 (COVID-19) pandemic, it is crucial to have an effective screening of infected patients to be isolated and treated. Chest X-Ray (CXR) radiological imaging coupled with Artificial Intelligence (AI) applications, in particular Convolutional Neural Network (CNN), can speed the COVID-19 diagnostic process. In this paper, we optimize the data augmentation and the CNN hyperparameters for detecting COVID-19 from CXRs in terms of validation accuracy. This optimization increases the accuracy of the popular CNN architectures such as the Visual Geometry Group network (VGG-19) and the Residual Neural Network (ResNet-50), by 11.93% and 4.97%, respectively. We then proposed CovidXrayNet model that is based on EfficientNet-B0 and our optimization results. We evaluated CovidXrayNet on two datasets, including our generated balanced COVIDcxr dataset (960 CXRs) and the benchmark COVIDx dataset (15,496 CXRs). With only 30 epochs of training, CovidXrayNet achieves state-of-the-art accuracy of 95.82% on the COVIDx dataset in the three-class classification task (COVID-19, normal or pneumonia). The CovidXRayNet model, the COVIDcxr dataset, and several optimization experiments are publicly available at https://github.com/MaramMonshi/CovidXrayNet. Twit Text FOAVip CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR ????Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33866253 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33866253 #FOAvip English Wikipedia <ref>{{Cite pmid|33866253}}</ref> CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR #MMPMID33866253 Monshi MMA; Poon J; Chung V; Monshi FM Comput Biol Med 2021[Jun]; 133 (ä): 104375 PMID33866253show ga To mitigate the spread of the current coronavirus disease 2019 (COVID-19) pandemic, it is crucial to have an effective screening of infected patients to be isolated and treated. Chest X-Ray (CXR) radiological imaging coupled with Artificial Intelligence (AI) applications, in particular Convolutional Neural Network (CNN), can speed the COVID-19 diagnostic process. In this paper, we optimize the data augmentation and the CNN hyperparameters for detecting COVID-19 from CXRs in terms of validation accuracy. This optimization increases the accuracy of the popular CNN architectures such as the Visual Geometry Group network (VGG-19) and the Residual Neural Network (ResNet-50), by 11.93% and 4.97%, respectively. We then proposed CovidXrayNet model that is based on EfficientNet-B0 and our optimization results. We evaluated CovidXrayNet on two datasets, including our generated balanced COVIDcxr dataset (960 CXRs) and the benchmark COVIDx dataset (15,496 CXRs). With only 30 epochs of training, CovidXrayNet achieves state-of-the-art accuracy of 95.82% on the COVIDx dataset in the three-class classification task (COVID-19, normal or pneumonia). The CovidXRayNet model, the COVIDcxr dataset, and several optimization experiments are publicly available at https://github.com/MaramMonshi/CovidXrayNet. |*COVID-19[MESH] |*Deep Learning[MESH] |Artificial Intelligence[MESH] |Humans[MESH] |Neural Networks, Computer[MESH] |SARS-CoV-2[MESH]CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for(epmc).pdf DeepDyve Pubget Overpricing