  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.
