RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray
Abstract 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 u...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-87994-2 |
| _version_ | 1818694485182251008 |
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| author | Saman Motamed Patrik Rogalla Farzad Khalvati |
| author_facet | Saman Motamed Patrik Rogalla Farzad Khalvati |
| author_sort | Saman Motamed |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-17T13:30:19Z |
| format | Article |
| id | doaj.art-b9f3d84cbfb7454aadf14305a9279e8b |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-17T13:30:19Z |
| publishDate | 2021-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b9f3d84cbfb7454aadf14305a9279e8b2022-12-21T21:46:35ZengNature PortfolioScientific Reports2045-23222021-04-0111111010.1038/s41598-021-87994-2RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-raySaman Motamed0Patrik Rogalla1Farzad Khalvati2Institute of Medical Science, University of TorontoUniversity Health NetworkInstitute of Medical Science, University of TorontoAbstract 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.https://doi.org/10.1038/s41598-021-87994-2 |
| spellingShingle | Saman Motamed Patrik Rogalla Farzad Khalvati RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-021-87994-2 |
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