Detection of disease on nasal breath sound by new lightweight architecture: Using COVID-19 as an example
The findings suggest that the proposed method holds significant potential for clinical implementation, advancing smartphone-based diagnostics in infectious diseases. The Dense-ReLU-Dropout model, combined with innovative feature processing techniques, offers a promising approach for efficient and accurate COVID-19 detection, showcasing the capabilities of mobile device-based diagnostics.
Authors
- Peiqi Li
- Ya-Jie Ji (ORCID: https://orcid.org/0000-0003-0852-6253)
- Renxing Li
- Zhuochang Yang (ORCID: https://orcid.org/0009-0000-0855-3767)
- Jiangang Chen (ORCID: https://orcid.org/0000-0001-8775-0282)
- Liping Gu (ORCID: https://orcid.org/0000-0002-9143-8221)
- Shengkai Li
- J. She
- Zhongxun Dong (ORCID: https://orcid.org/0000-0002-0061-2846)
- Zhao Tong (ORCID: https://orcid.org/0009-0005-1818-851X)
- Liang Feng
- Lin Shi (ORCID: https://orcid.org/0009-0005-5663-0858)
Institutions
- Shanghai University of Traditional Chinese Medicine (CN)
- Shanghai Sixth People's Hospital (CN)
- Shuguang Hospital (CN)
- Institute of Information Engineering (CN)
- East China Normal University (CN)
- Xi’an Jiaotong-Liverpool University (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251339284
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality