Papers › LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet...
LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet Deep Learning Algorithm
Yi Ma, Xinzi Xu, Qing Yu, Yuhang Zhang, Yongfu Li, Jian Zhao and Guoxing Wang
Improving access to health care services for the medically under-served population is vital to ensure that critical illness can be addressed immediately. In the scenarios where there is a severely lacking of skilled medical staff, a basic lung sound classification through a digital stethoscope can be used to provide an immediate diagnostic for respiratory-related diseases such as chronic obstructive pulmonary. In this work, we have developed an improved bi-ResNet deep learning architecture, LungBRN, which uses STFT and wavelet feature extraction techniques to mprove the accuracy compared to the state-of-the-art works. To ensure a fair evaluation, we have adopted the official benchmark standards and the “train-and-test” dataset splitting method stated in the ICBHI 2017 challenge. As a result, we are able to achieve a performance of 50.16%, which is the best result in terms of accuracy compared to all participating teams from ICBHI 2017.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Audio Classification | ICBHI Respiratory Sound Database | bi-ResNet (scratch) | ICBHI Score | 50.16 | #23 of 25 | Archive leaderboard | report |
| Audio Classification | ICBHI Respiratory Sound Database | bi-ResNet (scratch) | Sensitivity | 31.10 | #23 of 25 | Archive leaderboard | report |
| Audio Classification | ICBHI Respiratory Sound Database | bi-ResNet (scratch) | Specificity | 69.20 | #23 of 25 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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