Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data Augmentation

TL;DR

This paper presents a cross-dataset transfer learning method for COVID-19 detection using cough analysis, incorporating cough detection, segmentation, and data augmentation to significantly improve performance.

Contribution

It introduces a novel combination of cough detection, segmentation, and data augmentation techniques specifically for cross-dataset COVID-19 detection.

Findings

Significant performance improvement over baseline methods.

Alpha mixup augmentation is crucial for model enhancement.

Effective cough segmentation enhances detection accuracy.

Abstract

This paper addresses issues on cough-based COVID-19 detection. We propose a cross-dataset transfer learning approach to improve the performance of COVID-19 detection by incorporating cough detection, cough segmentation, and data augmentation. The first aimed at removing non-cough signals and cough signals with low probability. The second aimed at segregating several coughs in a waveform into individual coughs. The third aimed at increasing the number of samples for the deep learning model. These three processing blocks are important as our finding revealed a large margin of improvement relative to the baseline methods without these blocks. An ablation study is conducted to optimize hyperparameters and it was found that alpha mixup is an important factor among others in improving the model performance via this augmentation method. A summary of this study with previous studies on the same evaluation set was given to gain insights into different methods of cough-based COVID-19 detection.