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Article

Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19

1
Opticho, Pohang 37673, Korea
2
Department of Electrical Engineering, Creative IT Engineering, Mechanical Engineering, Graduate School of Artificial Intelligence, and Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
3
Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
4
Artificial Intelligence Application Research Section, Electronics and Telecommunications Research Institute (ETRI), Daegu 42994, Korea
*
Authors to whom correspondence should be addressed.
Electronics 2020, 9(9), 1388; https://doi.org/10.3390/electronics9091388
Received: 22 July 2020 / Revised: 15 August 2020 / Accepted: 23 August 2020 / Published: 27 August 2020
(This article belongs to the Special Issue Optical Sensing for Biomedical Applications)
The 2019 novel coronavirus (COVID-19) has spread rapidly all over the world. The standard test for screening COVID-19 patients is the polymerase chain reaction test. As this method is time consuming, as an alternative, chest X-rays may be considered for quick screening. However, specialization is required to read COVID-19 chest X-ray images as they vary in features. To address this, we present a multi-channel pre-trained ResNet architecture to facilitate the diagnosis of COVID-19 chest X-ray. Three ResNet-based models were retrained to classify X-rays in a one-against-all basis from (a) normal or diseased, (b) pneumonia or non-pneumonia, and (c) COVID-19 or non-COVID19 individuals. Finally, these three models were ensembled and fine-tuned using X-rays from 1579 normal, 4245 pneumonia, and 184 COVID-19 individuals to classify normal, pneumonia, and COVID-19 cases in a one-against-one framework. Our results show that the ensemble model is more accurate than the single model as it extracts more relevant semantic features for each class. The method provides a precision of 94% and a recall of 100%. It could potentially help clinicians in screening patients for COVID-19, thus facilitating immediate triaging and treatment for better outcomes. View Full-Text
Keywords: COVID-19; classification; deep learning; transfer learning; X-ray; ensemble learning COVID-19; classification; deep learning; transfer learning; X-ray; ensemble learning
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MDPI and ACS Style

Misra, S.; Jeon, S.; Lee, S.; Managuli, R.; Jang, I.-S.; Kim, C. Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19. Electronics 2020, 9, 1388. https://doi.org/10.3390/electronics9091388

AMA Style

Misra S, Jeon S, Lee S, Managuli R, Jang I-S, Kim C. Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19. Electronics. 2020; 9(9):1388. https://doi.org/10.3390/electronics9091388

Chicago/Turabian Style

Misra, Sampa, Seungwan Jeon, Seiyon Lee, Ravi Managuli, In-Su Jang, and Chulhong Kim. 2020. "Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19" Electronics 9, no. 9: 1388. https://doi.org/10.3390/electronics9091388

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