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10.1016/j.matpr.2021.05.553 http://scihub22266oqcxt.onion/10.1016/j.matpr.2021.05.553 34131560!8192882!34131560     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34131560&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Mater+Today+Proc 2021 ; ä (ä): ä Nephropedia Template TP {{tp|p=34131560|t=2021. Overview of deep learning models for identification Covid-19 |pdf=|usr=}}{{34131560}} gab.com Text Overview of deep learning models for identification Covid-19 JO: Mater Today Proc http://www.kidney.de/pget.php?&tw=1&pmid=34131560 #FOAvip The well-being and health of global population is continuously and badly affected by COVID-19 pandemic. Thus, to prevent the spread the pandemic between individuals, there is high importance in implementing automatic detection systems as rapid alternative diagnosis. The virus is affecting the person's respiratory system as well as creating white patchy shadows in the X-ray images of the lungs of individuals experiencing COVID-19. Also, deep learning can be defined as a useful and efficient AI technique used for analyzing chest X-ray images for reliable and effective screening of COVID-19; therefore, distinguishing people infected with COVID-19 and normal persons, and after that the infected individuals will be isolated for mitigating the virus spread. This study provides an overview regarding a few of the modern deep learning-based COVID-19, with design steps and types, also it compares the diagnostic method of COVID-19 with other methods of deep learning created with the use of radiology images. After a comparison between the most recent methods used in the previous works, it was found that RestNet50 pre-trained and DCNN model gives accuracy of 98%, which is the highest reported so far from among other proposed models were discussed in this paper. Twit Text FOAVip Overview of deep learning models for identification Covid-19 ????Mater Today Proc http://www.kidney.de/pget.php?&tw=1&pmid=34131560 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34131560 #FOAvip English Wikipedia <ref>{{Cite pmid|34131560}}</ref> Overview of deep learning models for identification Covid-19 #MMPMID34131560 Mohsin Ahmed H; Wael Abdullah B Mater Today Proc 2021[Jun]; ä (ä): ä PMID34131560show ga The well-being and health of global population is continuously and badly affected by COVID-19 pandemic. Thus, to prevent the spread the pandemic between individuals, there is high importance in implementing automatic detection systems as rapid alternative diagnosis. The virus is affecting the person's respiratory system as well as creating white patchy shadows in the X-ray images of the lungs of individuals experiencing COVID-19. Also, deep learning can be defined as a useful and efficient AI technique used for analyzing chest X-ray images for reliable and effective screening of COVID-19; therefore, distinguishing people infected with COVID-19 and normal persons, and after that the infected individuals will be isolated for mitigating the virus spread. This study provides an overview regarding a few of the modern deep learning-based COVID-19, with design steps and types, also it compares the diagnostic method of COVID-19 with other methods of deep learning created with the use of radiology images. After a comparison between the most recent methods used in the previous works, it was found that RestNet50 pre-trained and DCNN model gives accuracy of 98%, which is the highest reported so far from among other proposed models were discussed in this paper. äOverview of deep learning models for identification Covid-19 (epmc).pdf DeepDyve Pubget Overpricing