Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1007/s12559-020-09776-8 http://scihub22266oqcxt.onion/10.1007/s12559-020-09776-8 33488837!7812362!33488837     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       Cognit+Comput 2021 ; ä (ä): 1-17 Nephropedia Template TP {{tp|p=33488837|t=2021. COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting |pdf=|usr=}}{{33488837}} gab.com Text COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting JO: Cognit Comput http://www.kidney.de/pget.php?&tw=1&pmid=33488837 #FOAvip COVID-19 is an ongoing pandemic disease. To make more accurate diagnosis on COVID-19 than existing approaches, this paper proposed a novel method combining DenseNet and optimization of transfer learning setting (OTLS) strategy. Preprocessing was used to enhance, crop, and resize the collected chest CT images. Data augmentation method was used to increase the size of training set. A composite learning factor (CLF) was employed which assigned different learning factor to three types of layers: frozen layers, middle layers, and new layers. Meanwhile, the OTLS strategy was proposed. Finally, precomputation method was utilized to reduce RAM storage and accelerate the algorithm. We observed that optimization setting "201-IV" can achieve the best performance by proposed OTLS strategy. The sensitivity, specificity, precision, and accuracy of our proposed method were 96.35 +/- 1.07, 96.25 +/- 1.16, 96.29 +/- 1.11, and 96.30 +/- 0.56, respectively. The proposed DenseNet-OTLS method achieved better performances than state-of-the-art approaches in diagnosing COVID-19. Twit Text FOAVip COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting ????Cognit Comput http://www.kidney.de/pget.php?&tw=1&pmid=33488837 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33488837 #FOAvip English Wikipedia <ref>{{Cite pmid|33488837}}</ref> COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting #MMPMID33488837 Zhang YD; Satapathy SC; Zhang X; Wang SH Cognit Comput 2021[Jan]; ä (ä): 1-17 PMID33488837show ga COVID-19 is an ongoing pandemic disease. To make more accurate diagnosis on COVID-19 than existing approaches, this paper proposed a novel method combining DenseNet and optimization of transfer learning setting (OTLS) strategy. Preprocessing was used to enhance, crop, and resize the collected chest CT images. Data augmentation method was used to increase the size of training set. A composite learning factor (CLF) was employed which assigned different learning factor to three types of layers: frozen layers, middle layers, and new layers. Meanwhile, the OTLS strategy was proposed. Finally, precomputation method was utilized to reduce RAM storage and accelerate the algorithm. We observed that optimization setting "201-IV" can achieve the best performance by proposed OTLS strategy. The sensitivity, specificity, precision, and accuracy of our proposed method were 96.35 +/- 1.07, 96.25 +/- 1.16, 96.29 +/- 1.11, and 96.30 +/- 0.56, respectively. The proposed DenseNet-OTLS method achieved better performances than state-of-the-art approaches in diagnosing COVID-19. äCOVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning (epmc).pdf DeepDyve Pubget Overpricing