# DiCOVA-Net: Diagnosing COVID-19 using Acoustics based on Deep Residual Network for the DiCOVA Challenge 2021

arXiv: https://arxiv.org/abs/2107.06126 · HTML: https://mlsanity.com/paper/2107.06126/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 11 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Using Pre-Training Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1901.09960/) (2019)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [Improving Adversarial Robustness via Promoting Ensemble Diversity](https://mlsanity.com/paper/1901.08846/) (2019)
- [SpotTune: Transfer Learning through Adaptive Fine-tuning](https://mlsanity.com/paper/1811.08737/) (2018)
- [DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics](https://mlsanity.com/paper/2103.09148/) (2021)
- [RMDL: Random Multimodel Deep Learning for Classification](https://mlsanity.com/paper/1805.01890/) (2018)
- [A Powerful Generative Model Using Random Weights for the Deep Image Representation](https://mlsanity.com/paper/1606.04801/) (2016)

## Built on by (most cited first, 1 of 1)

- [UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio](https://mlsanity.com/paper/2204.07763/) (2022)

## Abstract (arXiv)

In this paper, we propose a deep residual network-based method, namely the DiCOVA-Net, to identify COVID-19 infected patients based on the acoustic recording of their coughs. Since there are far more healthy people than infected patients, this classification problem faces the challenge of imbalanced data. To improve the model's ability to recognize minority class (the infected patients), we introduce data augmentation and cost-sensitive methods into our model. Besides, considering the particularity of this task, we deploy some fine-tuning techniques to adjust the pre-training ResNet50. Furthermore, to improve the model's generalizability, we use ensemble learning to integrate prediction results from multiple base classifiers generated using different random seeds. To evaluate the proposed DiCOVA-Net's performance, we conducted experiments with the DiCOVA challenge dataset. The results show that our method has achieved 85.43% in AUC, among the top of all competing teams.