  SARS-CoV-2 is colloquially known as COVID-19 that had an initial outbreak in
December 2019. The deadly virus has spread across the world, taking part in the
global pandemic disease since March 2020. In addition, a recent variant of
SARS-CoV-2 named Delta is intractably contagious and responsible for more than
four million deaths over the world. Therefore, it is vital to possess a
self-testing service of SARS-CoV-2 at home. In this study, we introduce
Fruit-CoV, a two-stage vision framework, which is capable of detecting
SARS-CoV-2 infections through recorded cough sounds. Specifically, we convert
sounds into Log-Mel Spectrograms and use the EfficientNet-V2 network to extract
its visual features in the first stage. In the second stage, we use 14
convolutional layers extracted from the large-scale Pretrained Audio Neural
Networks for audio pattern recognition (PANNs) and the Wavegram-Log-Mel-CNN to
aggregate feature representations of the Log-Mel Spectrograms. Finally, we use
the combined features to train a binary classifier. In this study, we use a
dataset provided by the AICovidVN 115M Challenge, which includes a total of
7371 recorded cough sounds collected throughout Vietnam, India, and
Switzerland. Experimental results show that our proposed model achieves an AUC
score of 92.8% and ranks the 1st place on the leaderboard of the AICovidVN
Challenge. More importantly, our proposed framework can be integrated into a
call center or a VoIP system to speed up detecting SARS-CoV-2 infections
through online/recorded cough sounds.
