A Semi-Supervised Learning Using Tri-Classifier Model with Voting for COVID-19 Cough Classification

摘要

Due to the increasing severity of the COVID-19 pandemic, timely screening and diagnosis of infections are essential. Since cough is a common symptom of COVID-19, an AI-assisted cough classification scheme is designed in this paper to diagnose COVID-19 infection. To reduce the labeling efforts by human experts, a semi-supervised learning with voting scheme using a triple-classifier model is proposed for the COVID-19 cough classification. This work aims to improve the accuracy of the classification. Initially, the data pre-processing scheme is executed by performing data cleaning, resampling, and data enhancement so as to improve the audio quality before training. The pre-training scheme is then performed by using a few numbers of COVID-19 cough data with labeling. Then we modify a well-known self-supervised learning model, SimCLR, to a semi-supervised learning-based SimCLR-like model, which uses three different loss functions to fine-tune three training models for cough classification. Finally, a voting scheme is performed based on the classification results of the three cough classifiers so as to enhance the accuracy of the cough classification for COVID-19. The experiment results illustrate that the proposed scheme can achieve 85% accuracy, which outperforms the existing semi-supervised learning-based classification schemes.