Summary of Towards Reliable Respiratory Disease Diagnosis Based on Cough Sounds and Vision Transformers, by Qian Wang et al.
Towards reliable respiratory disease diagnosis based on cough sounds and vision transformers
by Qian Wang, Zhaoyang Bu, Jiaxuan Mao, Wenyu Zhu, Jingya Zhao, Wei Du, Guochao Shi, Min Zhou, Si Chen, Jieming Qu
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a unified framework to evaluate various deep learning models, from lightweight Convolutional Neural Networks (e.g., ResNet18) to modern vision transformers, for respiratory disease classification based on cough sound data. The authors aim to address the limitations of prior works by training and evaluating these models on a large-scale cough dataset. They compare the performance of different architectures, including traditional machine learning approaches, and propose a novel approach that combines self-supervised and supervised learning. Experimental results show that their proposed approach outperforms previous methods consistently across two benchmark datasets for COVID-19 diagnosis and one proprietary dataset for COPD/non-COPD classification, achieving an AUROC of 92.5%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to use deep learning models to diagnose respiratory diseases like COVID-19 and chronic obstructive pulmonary disease (COPD) based on the sounds of coughs. Usually, these models are trained and tested on small amounts of data because it’s hard to get and label larger datasets. The authors want to see which types of models work best for this task. They compare different architectures, from simple to complex, and propose a new approach that combines two learning methods. Their results show that their approach is better than previous ones at diagnosing these diseases. |
Keywords
» Artificial intelligence » Classification » Deep learning » Machine learning » Self supervised » Supervised
