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10.1007/s13246-020-00934-8 http://scihub22266oqcxt.onion/10.1007/s13246-020-00934-8 33025386!7537970!33025386     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 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Phys+Eng+Sci+Med 2020 ; 43 (4): 1289-1303 Nephropedia Template TP {{tp|p=33025386|t=2020. Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays |pdf=|usr=}}{{33025386}} gab.com Text Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays JO: Phys Eng Sci Med http://www.kidney.de/pget.php?&tw=1&pmid=33025386 #FOAvip Covid-19 first occurred in Wuhan, China in December 2019. Subsequently, the virus spread throughout the world and as of June 2020 the total number of confirmed cases are above 4.7 million with over 315,000 deaths. Machine learning algorithms built on radiography images can be used as a decision support mechanism to aid radiologists to speed up the diagnostic process. The aim of this work is to conduct a critical analysis to investigate the applicability of convolutional neural networks (CNNs) for the purpose of COVID-19 detection in chest X-ray images and highlight the issues of using CNN directly on the whole image. To accomplish this task, we use 12-off-the-shelf CNN architectures in transfer learning mode on 3 publicly available chest X-ray databases together with proposing a shallow CNN architecture in which we train it from scratch. Chest X-ray images are fed into CNN models without any preprocessing to replicate researches used chest X-rays in this manner. Then a qualitative investigation performed to inspect the decisions made by CNNs using a technique known as class activation maps (CAM). Using CAMs, one can map the activations contributed to the decision of CNNs back to the original image to visualize the most discriminating region(s) on the input image. We conclude that CNN decisions should not be taken into consideration, despite their high classification accuracy, until clinicians can visually inspect and approve the region(s) of the input image used by CNNs that lead to its prediction. Twit Text FOAVip Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays ????Phys Eng Sci Med http://www.kidney.de/pget.php?&tw=1&pmid=33025386 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33025386 #FOAvip English Wikipedia <ref>{{Cite pmid|33025386}}</ref> Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays #MMPMID33025386 Majeed T; Rashid R; Ali D; Asaad A Phys Eng Sci Med 2020[Dec]; 43 (4): 1289-1303 PMID33025386show ga Covid-19 first occurred in Wuhan, China in December 2019. Subsequently, the virus spread throughout the world and as of June 2020 the total number of confirmed cases are above 4.7 million with over 315,000 deaths. Machine learning algorithms built on radiography images can be used as a decision support mechanism to aid radiologists to speed up the diagnostic process. The aim of this work is to conduct a critical analysis to investigate the applicability of convolutional neural networks (CNNs) for the purpose of COVID-19 detection in chest X-ray images and highlight the issues of using CNN directly on the whole image. To accomplish this task, we use 12-off-the-shelf CNN architectures in transfer learning mode on 3 publicly available chest X-ray databases together with proposing a shallow CNN architecture in which we train it from scratch. Chest X-ray images are fed into CNN models without any preprocessing to replicate researches used chest X-rays in this manner. Then a qualitative investigation performed to inspect the decisions made by CNNs using a technique known as class activation maps (CAM). Using CAMs, one can map the activations contributed to the decision of CNNs back to the original image to visualize the most discriminating region(s) on the input image. We conclude that CNN decisions should not be taken into consideration, despite their high classification accuracy, until clinicians can visually inspect and approve the region(s) of the input image used by CNNs that lead to its prediction. |*Deep Learning[MESH] |*Neural Networks, Computer[MESH] |Artifacts[MESH] |COVID-19/*diagnosis/*diagnostic imaging/microbiology/virology[MESH] |Confidence Intervals[MESH] |Databases as Topic[MESH] |Humans[MESH] |Image Processing, Computer-Assisted[MESH] |SARS-CoV-2/physiology[MESH] |Thorax/*diagnostic imaging[MESH]Issues associated with deploying CNN transfer learning to detect COVID(epmc).pdf DeepDyve Pubget Overpricing