Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19
SCIE
SCOPUS
- Title
- Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19
- Authors
- Misra, Sampa; Jeon, Seungwan; Lee, Seiyon; Managuli, Ravi; Jang, In-Su; Kim, Chulhong
- Date Issued
- 2020-09
- Publisher
- MDPI AG
- Abstract
- 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.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/105560
- DOI
- 10.3390/electronics9091388
- ISSN
- 2079-9292
- Article Type
- Article
- Citation
- Electronics (Basel), vol. 9, no. 9, 2020-09
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- There are no files associated with this item.
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