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To test the possibility of differentiating chest x-ray images of COVID-19against other pneumonia and healthy patients using deep neural networks. Weconstruct the X-ray imaging data from two publicly available sources, whichinclude 5508 chest x-ray images across 2874 patients with four classes: normal,bacterial pneumonia, non-COVID-19 viral pneumonia, and COVID-19. To identifyCOVID-19, we propose a Focal Loss Based Neural Ensemble Network (FLANNEL), aflexible module to ensemble several convolutional neural network (CNN) modelsand fuse with a focal loss for accurate COVID-19 detection on class imbalancedata. FLANNEL consistently outperforms baseline models on COVID-19identification task in all metrics. Compared with the best baseline, FLANNELshows a higher macro-F1 score with 6% relative increase on Covid-19identification task where it achieves 0.7833(0.07) in Precision, 0.8609(0.03)in Recall, and 0.8168(0.03) F1 score.

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