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10.1016/j.scs.2020.102589 http://scihub22266oqcxt.onion/10.1016/j.scs.2020.102589 33169099!7642729!33169099     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sustain+Cities+Soc 2021 ; 65 (ä): 102589 Nephropedia Template TP {{tp|p=33169099|t=2021. Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey |pdf=|usr=}}{{33169099}} gab.com Text Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey JO: Sustain Cities Soc http://www.kidney.de/pget.php?&tw=1&pmid=33169099 #FOAvip Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities. Twit Text FOAVip Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey ????Sustain Cities Soc http://www.kidney.de/pget.php?&tw=1&pmid=33169099 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33169099 #FOAvip English Wikipedia <ref>{{Cite pmid|33169099}}</ref> Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey #MMPMID33169099 Bhattacharya S; Reddy Maddikunta PK; Pham QV; Gadekallu TR; Krishnan S SR; Chowdhary CL; Alazab M; Jalil Piran M Sustain Cities Soc 2021[Feb]; 65 (ä): 102589 PMID33169099show ga Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities. äDeep learning and medical image processing for coronavirus (COVID-19) (epmc).pdf DeepDyve Pubget Overpricing