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

arXiv: https://arxiv.org/abs/2010.16039 · HTML: https://mlsanity.com/paper/2010.16039/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models](https://mlsanity.com/paper/2103.03945/) (2021)
- [PyHealth: A Python Library for Health Predictive Models](https://mlsanity.com/paper/2101.04209/) (2021)
- [Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks](https://mlsanity.com/paper/2202.07679/) (2022)
- [An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2201.11440/) (2022)
- [Multi-loss ensemble deep learning for chest X-ray classification](https://mlsanity.com/paper/2109.14433/) (2021)
- [RICAU-Net: Residual-block Inspired Coordinate Attention U-Net for Segmentation of Small and Sparse Calcium Lesions in Cardiac CT](https://mlsanity.com/paper/2409.06993/) (2024)

## Abstract (arXiv)

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.