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10.1038/s41598-021-87994-2 http://scihub22266oqcxt.onion/10.1038/s41598-021-87994-2 33883609!8060427!33883609     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33883609&cmd=llinks): Failed to open stream: HTTP request failed! 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RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray |pdf=|usr=}}{{33883609}} gab.com Text RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33883609 #FOAvip COVID-19 spread across the globe at an immense rate and has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. Automation of COVID-19 testing using medical images can speed up the testing process of patients where health care systems lack sufficient numbers of the reverse-transcription polymerase chain reaction tests. Supervised deep learning models such as convolutional neural networks need enough labeled data for all classes to correctly learn the task of detection. Gathering labeled data is a cumbersome task and requires time and resources which could further strain health care systems and radiologists at the early stages of a pandemic such as COVID-19. In this study, we propose a randomized generative adversarial network (RANDGAN) that detects images of an unknown class (COVID-19) from known and labelled classes (Normal and Viral Pneumonia) without the need for labels and training data from the unknown class of images (COVID-19). We used the largest publicly available COVID-19 chest X-ray dataset, COVIDx, which is comprised of Normal, Pneumonia, and COVID-19 images from multiple public databases. In this work, we use transfer learning to segment the lungs in the COVIDx dataset. Next, we show why segmentation of the region of interest (lungs) is vital to correctly learn the task of classification, specifically in datasets that contain images from different resources as it is the case for the COVIDx dataset. Finally, we show improved results in detection of COVID-19 cases using our generative model (RANDGAN) compared to conventional generative adversarial networks for anomaly detection in medical images, improving the area under the ROC curve from 0.71 to 0.77. Twit Text FOAVip RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33883609 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33883609 #FOAvip English Wikipedia <ref>{{Cite pmid|33883609}}</ref> RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray #MMPMID33883609 Motamed S; Rogalla P; Khalvati F Sci Rep 2021[Apr]; 11 (1): 8602 PMID33883609show ga COVID-19 spread across the globe at an immense rate and has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. Automation of COVID-19 testing using medical images can speed up the testing process of patients where health care systems lack sufficient numbers of the reverse-transcription polymerase chain reaction tests. Supervised deep learning models such as convolutional neural networks need enough labeled data for all classes to correctly learn the task of detection. Gathering labeled data is a cumbersome task and requires time and resources which could further strain health care systems and radiologists at the early stages of a pandemic such as COVID-19. In this study, we propose a randomized generative adversarial network (RANDGAN) that detects images of an unknown class (COVID-19) from known and labelled classes (Normal and Viral Pneumonia) without the need for labels and training data from the unknown class of images (COVID-19). We used the largest publicly available COVID-19 chest X-ray dataset, COVIDx, which is comprised of Normal, Pneumonia, and COVID-19 images from multiple public databases. In this work, we use transfer learning to segment the lungs in the COVIDx dataset. Next, we show why segmentation of the region of interest (lungs) is vital to correctly learn the task of classification, specifically in datasets that contain images from different resources as it is the case for the COVIDx dataset. Finally, we show improved results in detection of COVID-19 cases using our generative model (RANDGAN) compared to conventional generative adversarial networks for anomaly detection in medical images, improving the area under the ROC curve from 0.71 to 0.77. |COVID-19/*diagnostic imaging[MESH] |Humans[MESH] |ROC Curve[MESH] |Radiographic Image Interpretation, Computer-Assisted/*methods[MESH] |Radiography, Thoracic/*methods[MESH]RANDGAN: Randomized generative adversarial network for detection of CO(epmc).pdf DeepDyve Pubget Overpricing