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10.1007/s11356-020-10133-3 http://scihub22266oqcxt.onion/10.1007/s11356-020-10133-3 32700269!7375456!32700269     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 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 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       Environ+Sci+Pollut+Res+Int 2020 ; 27 (29): 37155-37163 Nephropedia Template TP {{tp|p=32700269|t=2020. Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients |pdf=|usr=}}{{32700269}} gab.com Text Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients JO: Environ Sci Pollut Res Int http://www.kidney.de/pget.php?&tw=1&pmid=32700269 #FOAvip As the whole world is witnessing what novel coronavirus (COVID-19) can do to the mankind, it presents several unique features also. In the absence of specific vaccine for COVID-19, it is essential to detect the disease at an early stage and isolate an infected patient. Till today there is a global shortage of testing labs and testing kits for COVID-19. This paper discusses about the role of machine learning techniques for getting important insights like whether lung computed tomography (CT) scan should be the first screening/alternative test for real-time reverse transcriptase-polymerase chain reaction (RT-PCR), is COVID-19 pneumonia different from other viral pneumonia and if yes how to distinguish it using lung CT scan images from the carefully selected data of lung CT scan COVID-19-infected patients from the hospitals of Italy, China, Moscow and India? For training and testing the proposed system, custom vision software of Microsoft azure based on machine learning techniques is used. An overall accuracy of almost 91% is achieved for COVID-19 classification using the proposed methodology. Twit Text FOAVip Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients ????Environ Sci Pollut Res Int http://www.kidney.de/pget.php?&tw=1&pmid=32700269 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32700269 #FOAvip English Wikipedia <ref>{{Cite pmid|32700269}}</ref> Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients #MMPMID32700269 Sharma S Environ Sci Pollut Res Int 2020[Oct]; 27 (29): 37155-37163 PMID32700269show ga As the whole world is witnessing what novel coronavirus (COVID-19) can do to the mankind, it presents several unique features also. In the absence of specific vaccine for COVID-19, it is essential to detect the disease at an early stage and isolate an infected patient. Till today there is a global shortage of testing labs and testing kits for COVID-19. This paper discusses about the role of machine learning techniques for getting important insights like whether lung computed tomography (CT) scan should be the first screening/alternative test for real-time reverse transcriptase-polymerase chain reaction (RT-PCR), is COVID-19 pneumonia different from other viral pneumonia and if yes how to distinguish it using lung CT scan images from the carefully selected data of lung CT scan COVID-19-infected patients from the hospitals of Italy, China, Moscow and India? For training and testing the proposed system, custom vision software of Microsoft azure based on machine learning techniques is used. An overall accuracy of almost 91% is achieved for COVID-19 classification using the proposed methodology. |*Coronavirus Infections[MESH] |*Machine Learning[MESH] |*Pandemics[MESH] |*Pneumonia, Viral[MESH] |*Tomography, X-Ray Computed[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |China[MESH] |Humans[MESH] |India[MESH] |Italy[MESH] |Moscow[MESH]Drawing insights from COVID-19-infected patients using CT scan images (epmc).pdf DeepDyve Pubget Overpricing