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10.1016/j.ibmed.2021.100034 http://scihub22266oqcxt.onion/10.1016/j.ibmed.2021.100034 33899036!8057926!33899036     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       Intell+Based+Med 2021 ; 5 (ä): 100034 Nephropedia Template TP {{tp|p=33899036|t=2021. Optimal hyperparameter selection of deep learning models for COVID-19 chest X-ray classification |pdf=|usr=}}{{33899036}} gab.com Text Optimal hyperparameter selection of deep learning models for COVID-19 chest X-ray classification JO: Intell Based Med http://www.kidney.de/pget.php?&tw=1&pmid=33899036 #FOAvip The first and most critical response to curbing the spread of the novel coronavirus disease (COVID-19) is to deploy effective techniques to test potentially infected patients, isolate them and commence immediate treatment. However, several test kits currently in use are slow and in a shortage of supply. This paper presents techniques for diagnosing COVID-19 from chest X-ray (CXR) and address problems associated with training deep models with less voluminous datasets and class imbalance as obtained in most available CXR datasets on COVID-19. We used the discriminative fine-tuning approach, which dynamically assigns different learning rates to each layer of the network. The learning rate is set using the cyclical learning rate policy that changes per iteration. This flexibility ensured rapid convergence and avoided being stuck in saddle point plateau. In addition, we addressed the high computational demand of deep models by implementing our algorithm using the memory- and computational-efficient mixed-precision training. Despite the availability of scanty datasets, our model achieved high performance and generalisation. A Validation accuracy of 96.83%, sensitivity and specificity of 96.26% and 95.54% were obtained, respectively. When tested on an entirely new dataset, the model achieves 97% accuracy without further training. Lastly, we presented a visual interpretation of the model's output to prove that the model can aid radiologists in rapidly screening for the symptoms of COVID-19. Twit Text FOAVip Optimal hyperparameter selection of deep learning models for COVID-19 chest X-ray classification ????Intell Based Med http://www.kidney.de/pget.php?&tw=1&pmid=33899036 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33899036 #FOAvip English Wikipedia <ref>{{Cite pmid|33899036}}</ref> Optimal hyperparameter selection of deep learning models for COVID-19 chest X-ray classification #MMPMID33899036 Adedigba AP; Adeshina SA; Aina OE; Aibinu AM Intell Based Med 2021[]; 5 (ä): 100034 PMID33899036show ga The first and most critical response to curbing the spread of the novel coronavirus disease (COVID-19) is to deploy effective techniques to test potentially infected patients, isolate them and commence immediate treatment. However, several test kits currently in use are slow and in a shortage of supply. This paper presents techniques for diagnosing COVID-19 from chest X-ray (CXR) and address problems associated with training deep models with less voluminous datasets and class imbalance as obtained in most available CXR datasets on COVID-19. We used the discriminative fine-tuning approach, which dynamically assigns different learning rates to each layer of the network. The learning rate is set using the cyclical learning rate policy that changes per iteration. This flexibility ensured rapid convergence and avoided being stuck in saddle point plateau. In addition, we addressed the high computational demand of deep models by implementing our algorithm using the memory- and computational-efficient mixed-precision training. Despite the availability of scanty datasets, our model achieved high performance and generalisation. A Validation accuracy of 96.83%, sensitivity and specificity of 96.26% and 95.54% were obtained, respectively. When tested on an entirely new dataset, the model achieves 97% accuracy without further training. Lastly, we presented a visual interpretation of the model's output to prove that the model can aid radiologists in rapidly screening for the symptoms of COVID-19. äOptimal hyperparameter selection of deep learning models for COVID-19 (epmc).pdf DeepDyve Pubget Overpricing