Towards reliable respiratory disease diagnosis based on cough sounds and vision transformers
This work addresses the challenge of reliable respiratory disease diagnosis for medical applications, but it is incremental as it builds on existing deep learning methods with a focus on model comparison and data scaling.
The authors tackled the problem of respiratory disease diagnosis using cough sounds by creating a unified framework to evaluate various deep models and proposing a novel approach combining self-supervised and supervised learning on a large-scale dataset, achieving an AUROC of 92.5% on benchmark datasets for COVID-19 and COPD classification.
Recent advancements in deep learning techniques have sparked performance boosts in various real-world applications including disease diagnosis based on multi-modal medical data. Cough sound data-based respiratory disease (e.g., COVID-19 and Chronic Obstructive Pulmonary Disease) diagnosis has also attracted much attention. However, existing works usually utilise traditional machine learning or deep models of moderate scales. On the other hand, the developed approaches are trained and evaluated on small-scale data due to the difficulty of curating and annotating clinical data on scale. To address these issues in prior works, we create a unified framework to evaluate various deep models from lightweight Convolutional Neural Networks (e.g., ResNet18) to modern vision transformers and compare their performance in respiratory disease classification. Based on the observations from such an extensive empirical study, we propose a novel approach to cough-based disease classification based on both self-supervised and supervised learning on a large-scale cough data set. Experimental results demonstrate our proposed approach outperforms prior arts consistently on two benchmark datasets for COVID-19 diagnosis and a proprietary dataset for COPD/non-COPD classification with an AUROC of 92.5%.