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RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray

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 medi...

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Autores principales: Motamed, Saman, Rogalla, Patrik, Khalvati, Farzad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8060427/
https://www.ncbi.nlm.nih.gov/pubmed/33883609
http://dx.doi.org/10.1038/s41598-021-87994-2
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author Motamed, Saman
Rogalla, Patrik
Khalvati, Farzad
author_facet Motamed, Saman
Rogalla, Patrik
Khalvati, Farzad
author_sort Motamed, Saman
collection PubMed
description 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.
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spelling pubmed-80604272021-04-23 RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray Motamed, Saman Rogalla, Patrik Khalvati, Farzad Sci Rep Article 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. Nature Publishing Group UK 2021-04-21 /pmc/articles/PMC8060427/ /pubmed/33883609 http://dx.doi.org/10.1038/s41598-021-87994-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Motamed, Saman
Rogalla, Patrik
Khalvati, Farzad
RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray
title RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray
title_full RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray
title_fullStr RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray
title_full_unstemmed RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray
title_short RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray
title_sort randgan: randomized generative adversarial network for detection of covid-19 in chest x-ray
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8060427/
https://www.ncbi.nlm.nih.gov/pubmed/33883609
http://dx.doi.org/10.1038/s41598-021-87994-2
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