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10.1007/s42979-021-00695-5 http://scihub22266oqcxt.onion/10.1007/s42979-021-00695-5 34075355!8152712!34075355     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       SN+Comput+Sci 2021 ; 2 (4): 300 Nephropedia Template TP {{tp|p=34075355|t=2021. Chest X-ray Classification Using Deep Learning for Automated COVID-19 Screening |pdf=|usr=}}{{34075355}} gab.com Text Chest X-ray Classification Using Deep Learning for Automated COVID-19 Screening JO: SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=34075355 #FOAvip In today's world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. When the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into four classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on a severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG-16 with a test accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with a test accuracy of 98.9 %, whereas the ResNet-18 worked best for severity classification achieving a test accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42979-021-00695-5. Twit Text FOAVip Chest X-ray Classification Using Deep Learning for Automated COVID-19 Screening ????SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=34075355 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34075355 #FOAvip English Wikipedia <ref>{{Cite pmid|34075355}}</ref> Chest X-ray Classification Using Deep Learning for Automated COVID-19 Screening #MMPMID34075355 Shelke A; Inamdar M; Shah V; Tiwari A; Hussain A; Chafekar T; Mehendale N SN Comput Sci 2021[]; 2 (4): 300 PMID34075355show ga In today's world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. When the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into four classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on a severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG-16 with a test accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with a test accuracy of 98.9 %, whereas the ResNet-18 worked best for severity classification achieving a test accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42979-021-00695-5. äChest X-ray Classification Using Deep Learning for Automated COVID-19 (epmc).pdf DeepDyve Pubget Overpricing