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10.1016/j.ijleo.2021.166405 http://scihub22266oqcxt.onion/10.1016/j.ijleo.2021.166405 33551492!7848537!33551492     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 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       Optik+(Stuttg) 2021 ; 231 (ä): 166405 Nephropedia Template TP {{tp|p=33551492|t=2021. COVIDetection-Net: A tailored COVID-19 detection from chest radiography images using deep learning |pdf=|usr=}}{{33551492}} gab.com Text COVIDetection-Net: A tailored COVID-19 detection from chest radiography images using deep learning JO: Optik (Stuttg) http://www.kidney.de/pget.php?&tw=1&pmid=33551492 #FOAvip In this study, a medical system based on Deep Learning (DL) which we called "COVIDetection-Net" is proposed for automatic detection of new corona virus disease 2019 (COVID-19) infection from chest radiography images (CRIs). The proposed system is based on ShuffleNet and SqueezeNet architecture to extract deep learned features and Multiclass Support Vector Machines (MSVM) for detection and classification. Our dataset contains 1200 CRIs that collected from two different publicly available databases. Extensive experiments were carried out using the proposed model. The highest detection accuracy of 100 % for COVID/NonCOVID, 99.72 % for COVID/Normal/pneumonia and 94.44 % for COVID/Normal/Bacterial pneumonia/Viral pneumonia have been obtained. The proposed system superior all published methods in recall, specificity, precision, F1-Score and accuracy. Confusion Matrix (CM) and Receiver Operation Characteristics (ROC) analysis are also used to depict the performance of the proposed model. Hence the proposed COVIDetection-Net can serve as an efficient system in the current state of COVID-19 pandemic and can be used in everywhere that are facing shortage of test kits. Twit Text FOAVip COVIDetection-Net: A tailored COVID-19 detection from chest radiography images using deep learning ????Optik (Stuttg) http://www.kidney.de/pget.php?&tw=1&pmid=33551492 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33551492 #FOAvip English Wikipedia <ref>{{Cite pmid|33551492}}</ref> COVIDetection-Net: A tailored COVID-19 detection from chest radiography images using deep learning #MMPMID33551492 Elkorany AS; Elsharkawy ZF Optik (Stuttg) 2021[Apr]; 231 (ä): 166405 PMID33551492show ga In this study, a medical system based on Deep Learning (DL) which we called "COVIDetection-Net" is proposed for automatic detection of new corona virus disease 2019 (COVID-19) infection from chest radiography images (CRIs). The proposed system is based on ShuffleNet and SqueezeNet architecture to extract deep learned features and Multiclass Support Vector Machines (MSVM) for detection and classification. Our dataset contains 1200 CRIs that collected from two different publicly available databases. Extensive experiments were carried out using the proposed model. The highest detection accuracy of 100 % for COVID/NonCOVID, 99.72 % for COVID/Normal/pneumonia and 94.44 % for COVID/Normal/Bacterial pneumonia/Viral pneumonia have been obtained. The proposed system superior all published methods in recall, specificity, precision, F1-Score and accuracy. Confusion Matrix (CM) and Receiver Operation Characteristics (ROC) analysis are also used to depict the performance of the proposed model. Hence the proposed COVIDetection-Net can serve as an efficient system in the current state of COVID-19 pandemic and can be used in everywhere that are facing shortage of test kits. äCOVIDetection-Net: A tailored COVID-19 detection from chest radiograph(epmc).pdf DeepDyve Pubget Overpricing