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10.1016/j.compmedimag.2021.101921 http://scihub22266oqcxt.onion/10.1016/j.compmedimag.2021.101921 33930734!8062905!33930734     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 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 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Med+Imaging+Graph 2021 ; 90 (ä): 101921 Nephropedia Template TP {{tp|p=33930734|t=2021. Evaluation of deep learning approaches for identification of different corona-virus species and time series prediction |pdf=|usr=}}{{33930734}} gab.com Text Evaluation of deep learning approaches for identification of different corona-virus species and time series prediction JO: Comput Med Imaging Graph http://www.kidney.de/pget.php?&tw=1&pmid=33930734 #FOAvip Novel corona-virus (nCOV) has been declared as a pandemic that started from the city Wuhan of China. This deadly virus is infecting people rapidly and has targeted 4.93 million people across the world, with 227?K people being infected only in Italy. Cases of nCOV are quickly increasing whereas the number of nCOV test kits available in hospitals are limited. Under these conditions, an automated system for the classification of patients into nCOV positive and negative cases, is a much needed tool against the pandemic, helping in a selective use of the limited number of test kits. In this research, Convolutional Neural Network-based models (one block VGG, two block VGG, three block VGG, four block VGG, LetNet-5, AlexNet, and Resnet-50) have been employed for the detection of Corona-virus and SARS_MERS infected patients, distinguishing them from the healthy subjects, using lung X-ray scans, which has proven to be a challenging task, due to overlapping characteristics of different corona virus types. Furthermore, LSTM model has been used for time series forecasting of nCOV cases, in the following 10 days, in Italy. The evaluation results obtained, proved that the VGG1 model distinguishes the three classes at an accuracy of almost 91%, as compared to other models, whereas the approach based on the LSTM predicts the number of nCOV cases with 99% accuracy. Twit Text FOAVip Evaluation of deep learning approaches for identification of different corona-virus species and time series prediction ????Comput Med Imaging Graph http://www.kidney.de/pget.php?&tw=1&pmid=33930734 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33930734 #FOAvip English Wikipedia <ref>{{Cite pmid|33930734}}</ref> Evaluation of deep learning approaches for identification of different corona-virus species and time series prediction #MMPMID33930734 Younis MC Comput Med Imaging Graph 2021[Jun]; 90 (ä): 101921 PMID33930734show ga Novel corona-virus (nCOV) has been declared as a pandemic that started from the city Wuhan of China. This deadly virus is infecting people rapidly and has targeted 4.93 million people across the world, with 227?K people being infected only in Italy. Cases of nCOV are quickly increasing whereas the number of nCOV test kits available in hospitals are limited. Under these conditions, an automated system for the classification of patients into nCOV positive and negative cases, is a much needed tool against the pandemic, helping in a selective use of the limited number of test kits. In this research, Convolutional Neural Network-based models (one block VGG, two block VGG, three block VGG, four block VGG, LetNet-5, AlexNet, and Resnet-50) have been employed for the detection of Corona-virus and SARS_MERS infected patients, distinguishing them from the healthy subjects, using lung X-ray scans, which has proven to be a challenging task, due to overlapping characteristics of different corona virus types. Furthermore, LSTM model has been used for time series forecasting of nCOV cases, in the following 10 days, in Italy. The evaluation results obtained, proved that the VGG1 model distinguishes the three classes at an accuracy of almost 91%, as compared to other models, whereas the approach based on the LSTM predicts the number of nCOV cases with 99% accuracy. |*Deep Learning[MESH] |Betacoronavirus/*classification[MESH] |COVID-19/epidemiology/mortality/virology[MESH] |Humans[MESH] |Middle East Respiratory Syndrome Coronavirus/classification[MESH] |Pneumonia, Viral/epidemiology/mortality/virology[MESH]Evaluation of deep learning approaches for identification of different(epmc).pdf DeepDyve Pubget Overpricing