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10.1016/j.matpr.2021.01.820 http://scihub22266oqcxt.onion/10.1016/j.matpr.2021.01.820 33564622!7862904!33564622     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       Mater+Today+Proc 2021 ; ä (ä): ä Nephropedia Template TP {{tp|p=33564622|t=2021. AUTOMATIC OPTIMIZED CNN BASED COVID-19 LUNG INFECTION SEGMENTATION FROM CT IMAGE |pdf=|usr=}}{{33564622}} gab.com Text AUTOMATIC OPTIMIZED CNN BASED COVID-19 LUNG INFECTION SEGMENTATION FROM CT IMAGE JO: Mater Today Proc http://www.kidney.de/pget.php?&tw=1&pmid=33564622 #FOAvip In early 2020, the corona virus disease (COVID-19) has become a global epidemic. The WHO announced the disease as a public health emergency of international importance (PHEIC), and the issue was considered a health emergency. Automated computed tomography (CD) detection of lung infections offers a tremendous opportunity to expand the traditional health approach to resolving COVID-19. But many problems with CT. Facing contaminated areas from fragments, which include greater variability in infectious properties and low-intensity comparison between infections and normal tissues. Moreover, by suppressing the project of an in-depth model, a lot of information cannot be collected over some time. Twit Text FOAVip AUTOMATIC OPTIMIZED CNN BASED COVID-19 LUNG INFECTION SEGMENTATION FROM CT IMAGE ????Mater Today Proc http://www.kidney.de/pget.php?&tw=1&pmid=33564622 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33564622 #FOAvip English Wikipedia <ref>{{Cite pmid|33564622}}</ref> AUTOMATIC OPTIMIZED CNN BASED COVID-19 LUNG INFECTION SEGMENTATION FROM CT IMAGE #MMPMID33564622 Priya C; Sithi Shameem Fathima SMH; Kirubanandasarathy N; Valanarasid A; Safana Begam MH; Aiswarya N Mater Today Proc 2021[Feb]; ä (ä): ä PMID33564622show ga In early 2020, the corona virus disease (COVID-19) has become a global epidemic. The WHO announced the disease as a public health emergency of international importance (PHEIC), and the issue was considered a health emergency. Automated computed tomography (CD) detection of lung infections offers a tremendous opportunity to expand the traditional health approach to resolving COVID-19. But many problems with CT. Facing contaminated areas from fragments, which include greater variability in infectious properties and low-intensity comparison between infections and normal tissues. Moreover, by suppressing the project of an in-depth model, a lot of information cannot be collected over some time. äAUTOMATIC OPTIMIZED CNN BASED COVID-19 LUNG INFECTION SEGMENTATION FRO(epmc).pdf DeepDyve Pubget Overpricing