End-2-End COVID-19 Detection from Breath & Cough Audio
This research offers a potential low-cost and scalable method for rapid COVID-19 testing at a population scale, which could benefit public health efforts.
This paper presents the first attempt to diagnose COVID-19 using end-to-end deep learning from crowd-sourced audio samples, achieving a ROC-AUC of 0.846. The proposed model, COVID-19 Identification ResNet (CIdeR), uses a novel modeling strategy from a joint breath and cough representation.
Our main contributions are as follows: (I) We demonstrate the first attempt to diagnose COVID-19 using end-to-end deep learning from a crowd-sourced dataset of audio samples, achieving ROC-AUC of 0.846; (II) Our model, the COVID-19 Identification ResNet, (CIdeR), has potential for rapid scalability, minimal cost and improving performance as more data becomes available. This could enable regular COVID-19 testing at apopulation scale; (III) We introduce a novel modelling strategy using a custom deep neural network to diagnose COVID-19 from a joint breath and cough representation; (IV) We release our four stratified folds for cross parameter optimisation and validation on a standard public corpus and details on the models for reproducibility and future reference.