Virufy: A Multi-Branch Deep Learning Network for Automated Detection of COVID-19
This addresses the need for fast, affordable, and accessible COVID-19 testing, especially in low-resource settings, by enabling deployment on smartphones, though it is incremental as it builds on prior audio-based detection methods.
The paper tackled the problem of detecting COVID-19 from audio samples collected on smartphones, which often have suboptimal quality compared to clinical data, by proposing a multi-branch deep learning network trained on crowdsourced data. The result was state-of-the-art performance on the COUGHVID dataset, achieving an AUC of 0.99 for COVID-19 positive samples.
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.