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

• URL: http://arxiv.org/abs/2005.05576v1
• Date: Tue, 12 May 2020 07:03:46 GMT
• Title: Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19
• Authors: Sampa Misra, Seungwan Jeon, Seiyon Lee, Ravi Managuli, and Chulhong Kim
• Abstract summary: The current gold standard test for screening COVID-19 patients is the polymerase chain reaction test. As an alternative, chest X-rays are being considered for quick screening. We present a multi-channel transfer learning model based on ResNet architecture to facilitate the diagnosis of COVID-19 chest X-rays.
• Score: 6.221369419104751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The 2019 novel coronavirus (COVID-19) has spread rapidly all over the world and it is affecting the whole society. The current gold standard test for screening COVID-19 patients is the polymerase chain reaction test. However, the COVID-19 test kits are not widely available and time-consuming. Thus, as an alternative, chest X-rays are being considered for quick screening. Since the presentation of COVID-19 in chest X-rays is varied in features and specialization in reading COVID-19 chest X-rays are required thus limiting its use for diagnosis. To address this challenge of reading chest X-rays by radiologists quickly, we present a multi-channel transfer learning model based on ResNet architecture to facilitate the diagnosis of COVID-19 chest X-ray. Three ResNet-based models (Models a, b, and c) were retrained using Dataset_A (1579 normal and 4429 diseased), Dataset_B (4245 pneumonia and 1763 non-pneumonia), and Dataset_C (184 COVID-19 and 5824 Non-COVID19), respectively, to classify (a) normal or diseased, (b) pneumonia or non-pneumonia, and (c) COVID-19 or non-COVID19. Finally, these three models were ensembled and fine-tuned using Dataset_D (1579 normal, 4245 pneumonia, and 184 COVID-19) to classify normal, pneumonia, and COVID-19 cases. Our results show that the ensemble model is more accurate than the single ResNet model, which is also re-trained using Dataset_D as it extracts more relevant semantic features for each class. Our approach provides a precision of 94 % and a recall of 100%. Thus, our method could potentially help clinicians in screening patients for COVID-19, thus facilitating immediate triaging and treatment for better outcomes.

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