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

Qiao, Zhi; Bae, Austin; Glass, Lucas M; Xiao, Cao; Sun, Jimeng

OBJECTIVE: To test the possibility of differentiating chest x-ray images of COVID-19 against other pneumonia and healthy patients using deep neural networks. MATERIALS AND METHODS: 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. RESULTS: 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. DISCUSSION: Ensemble learning that combines multiple independent basis classifiers can increase the robustness and accuracy. We propose Neural Weighing Module to learn importance weight for each base model and combine them via weighted ensemble to get the final classification results. In order to handle the class imbalance challenge, we adapt Focal loss to our multiple classification task as the loss function. CONCLUSION: FLANNEL effectively combines state-of-the-art CNN classification models and tackle class imbalance with Focal loss to achieve better performance on Covid-19 detection from X-rays.