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

TL;DR

This paper introduces FLANNEL, a neural network ensemble using focal loss to improve COVID-19 detection from chest X-ray images, outperforming baseline models on class imbalance data.

Contribution

The paper proposes a novel ensemble neural network with focal loss specifically designed for COVID-19 detection in chest X-ray images, addressing class imbalance issues.

Findings

FLANNEL outperforms baseline models in all metrics.

Achieves a 6% relative increase in macro-F1 score for COVID-19 detection.

Provides higher precision, recall, and F1 scores compared to existing methods.

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.