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10.1007/s42979-021-00690-w http://scihub22266oqcxt.onion/10.1007/s42979-021-00690-w 34056622!8144280!34056622     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       SN+Comput+Sci 2021 ; 2 (4): 294 Nephropedia Template TP {{tp|p=34056622|t=2021. Automated COVID-19 Detection from Chest X-Ray Images: A High-Resolution Network (HRNet) Approach |pdf=|usr=}}{{34056622}} gab.com Text Automated COVID-19 Detection from Chest X-Ray Images: A High-Resolution Network (HRNet) Approach JO: SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=34056622 #FOAvip The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the non-invasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. Our intensive background studies show that, the works till now are not efficient to produce an unbiased detection result. In this work, we proposed an automated COVID-19 classification method, utilizing available COVID and non-COVID X-Ray datasets, along with High-Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed method, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got a significant result of 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Finally, we conclude that our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease. Twit Text FOAVip Automated COVID-19 Detection from Chest X-Ray Images: A High-Resolution Network (HRNet) Approach ????SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=34056622 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34056622 #FOAvip English Wikipedia <ref>{{Cite pmid|34056622}}</ref> Automated COVID-19 Detection from Chest X-Ray Images: A High-Resolution Network (HRNet) Approach #MMPMID34056622 Ahmed S; Hossain T; Hoque OB; Sarker S; Rahman S; Shah FM SN Comput Sci 2021[]; 2 (4): 294 PMID34056622show ga The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the non-invasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. Our intensive background studies show that, the works till now are not efficient to produce an unbiased detection result. In this work, we proposed an automated COVID-19 classification method, utilizing available COVID and non-COVID X-Ray datasets, along with High-Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed method, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got a significant result of 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Finally, we conclude that our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease. äAutomated COVID-19 Detection from Chest X-Ray Images: A High-Resolutio(epmc).pdf DeepDyve Pubget Overpricing