FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection

• URL: http://arxiv.org/abs/2010.16039v1
• Date: Fri, 30 Oct 2020 03:17:31 GMT
• Title: FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection
• Authors: Zhi Qiao, Austin Bae, Lucas M. Glass, Cao Xiao, and Jimeng Sun
• Abstract summary: We construct the X-ray imaging data from 2874 patients with four classes: normal, bacterial pneumonia, non-COVID-19 viral pneumonia, and COVID-19. To identify COVID-19, we propose a Focal Loss Based Neural Ensemble Network (FLANNEL) FLANNEL consistently outperforms baseline models on COVID-19 identification task in all metrics.
• Score: 61.04937460198252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To test the possibility of differentiating chest x-ray images of COVID-19 against other pneumonia and healthy patients using deep neural networks. We construct the X-ray imaging data from two publicly available sources, which include 5508 chest x-ray images across 2874 patients with four classes: normal, bacterial pneumonia, non-COVID-19 viral pneumonia, and COVID-19. To identify COVID-19, we propose a Focal Loss Based Neural Ensemble Network (FLANNEL), a flexible module to ensemble several convolutional neural network (CNN) models and fuse with a focal loss for accurate COVID-19 detection on class imbalance data. FLANNEL consistently outperforms baseline models on COVID-19 identification task in all metrics. Compared with the best baseline, FLANNEL shows a higher macro-F1 score with 6% relative increase on Covid-19 identification task where it achieves 0.7833(0.07) in Precision, 0.8609(0.03) in Recall, and 0.8168(0.03) F1 score.

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