End-to-End Network Based on Transformer for Automatic Detection of Covid-19

Cong Cai, Bin Liu, Jianhua Tao, Zhengkun Tian, Jiahao Lu, Kexin Wang

Abstract: The novel coronavirus disease (COVID-19) was declared a pandemic by the World Health Organization. The cumulative number of deaths is more than 4.8 million. Epidemiology experts concur that mass testing is essential for isolating infected individuals, contact tracing, and slowing the progression of the virus. In recent months, some machine learning methods have been proposed utilizing audio cues for COVID-19 detection. However, many works are based on hand-crafted features and deep features to detect COVID-19. There is no evidence that these features are optimal for COVID-19 detection. Therefore, we proposed an end-to-end network based on transformer for automatic detection of COVID-19. It directly learns features from the raw waveform for end-to-end learning, rather than extracting features in advance. We propose a feature extraction module to automatically extract features. And we use the transformer architectures to model the dependencies between the extracted features. It is the first end-to-end learning based on raw waveform for COVID-19 detection. Experiments on COUGHVID dataset show that our method has achieved competitive results.