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10.1007/s12559-020-09785-7 http://scihub22266oqcxt.onion/10.1007/s12559-020-09785-7 33520007!7829098!33520007     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       Cognit+Comput 2021 ; ä (ä): 1-16 Nephropedia Template TP {{tp|p=33520007|t=2021. Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network |pdf=|usr=}}{{33520007}} gab.com Text Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network JO: Cognit Comput http://www.kidney.de/pget.php?&tw=1&pmid=33520007 #FOAvip The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis. Automatic identification software tools are essential for helping to screen COVID-19 using CT images. However, there are few datasets available, making it difficult to train deep learning (DL) networks. To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images. The Whale Optimization Algorithm (WOA) is used to optimize the hyperparameters of GAN's generator. The proposed method is tested and validated with different classification and meta-heuristics algorithms using the SARS-CoV-2 CT-Scan dataset, consisting of COVID-19 and non-COVID-19 images. The performance metrics of the proposed optimized model, including accuracy (99.22%), sensitivity (99.78%), specificity (97.78%), F1-score (98.79%), positive predictive value (97.82%), and negative predictive value (99.77%), as well as its confusion matrix and receiver operating characteristic (ROC) curves, indicate that it performs better than state-of-the-art methods. This proposed model will help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks. Twit Text FOAVip Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network ????Cognit Comput http://www.kidney.de/pget.php?&tw=1&pmid=33520007 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33520007 #FOAvip English Wikipedia <ref>{{Cite pmid|33520007}}</ref> Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network #MMPMID33520007 Goel T; Murugan R; Mirjalili S; Chakrabartty DK Cognit Comput 2021[Jan]; ä (ä): 1-16 PMID33520007show ga The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis. Automatic identification software tools are essential for helping to screen COVID-19 using CT images. However, there are few datasets available, making it difficult to train deep learning (DL) networks. To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images. The Whale Optimization Algorithm (WOA) is used to optimize the hyperparameters of GAN's generator. The proposed method is tested and validated with different classification and meta-heuristics algorithms using the SARS-CoV-2 CT-Scan dataset, consisting of COVID-19 and non-COVID-19 images. The performance metrics of the proposed optimized model, including accuracy (99.22%), sensitivity (99.78%), specificity (97.78%), F1-score (98.79%), positive predictive value (97.82%), and negative predictive value (99.77%), as well as its confusion matrix and receiver operating characteristic (ROC) curves, indicate that it performs better than state-of-the-art methods. This proposed model will help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks. äAutomatic Screening of COVID-19 Using an Optimized Generative Adversar(epmc).pdf DeepDyve Pubget Overpricing