# Virufy: A Multi-Branch Deep Learning Network for Automated Detection of COVID-19

arXiv: https://arxiv.org/abs/2103.01806 · HTML: https://mlsanity.com/paper/2103.01806/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 2 Mar 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data](https://mlsanity.com/paper/2006.05919/) (2020)
- [The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms](https://mlsanity.com/paper/2009.11644/) (2020)
- [Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis](https://mlsanity.com/paper/2005.10548/) (2020)
- [AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App](https://mlsanity.com/paper/2004.01275/) (2020)
- [Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds](https://mlsanity.com/paper/2009.08790/) (2020)
- [Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough](https://mlsanity.com/paper/2011.13320/) (2020)
- [COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis](https://mlsanity.com/paper/2003.11117/) (2020)
- [An Overview on Audio, Signal, Speech, & Language Processing for COVID-19](https://mlsanity.com/paper/2005.08579/) (2020)

## Built on by (most cited first, 7 of 7)

- [Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey](https://mlsanity.com/paper/2309.14383/) (2023)
- [Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation](https://mlsanity.com/paper/2501.01117/) (2025)
- [XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization](https://mlsanity.com/paper/2508.14949/) (2025)
- [An Ensemble-based Multi-Criteria Decision Making Method for COVID-19 Cough Classification](https://mlsanity.com/paper/2110.00508/) (2021)
- [Fused Audio Instance and Representation for Respiratory Disease Detection](https://mlsanity.com/paper/2204.10581/) (2022)
- [An AI-enabled Bias-Free Respiratory Disease Diagnosis Model using Cough Audio: A Case Study for COVID-19](https://mlsanity.com/paper/2401.02996/) (2024)
- [A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Disease](https://mlsanity.com/paper/2508.16237/) (2025)

## Abstract (arXiv)

Fast and affordable solutions for COVID-19 testing are necessary to contain the spread of the global pandemic and help relieve the burden on medical facilities. Currently, limited testing locations and expensive equipment pose difficulties for individuals trying to be tested, especially in low-resource settings. Researchers have successfully presented models for detecting COVID-19 infection status using audio samples recorded in clinical settings [5, 15], suggesting that audio-based Artificial Intelligence models can be used to identify COVID-19. Such models have the potential to be deployed on smartphones for fast, widespread, and low-resource testing. However, while previous studies have trained models on cleaned audio samples collected mainly from clinical settings, audio samples collected from average smartphones may yield suboptimal quality data that is different from the clean data that models were trained on. This discrepancy may add a bias that affects COVID-19 status predictions. To tackle this issue, we propose a multi-branch deep learning network that is trained and tested on crowdsourced data where most of the data has not been manually processed and cleaned. Furthermore, the model achieves state-of-art results for the COUGHVID dataset [16]. After breaking down results for each category, we have shown an AUC of 0.99 for audio samples with COVID-19 positive labels.