Papers › LungAttn: advanced lung sound classification using attention mechanism with dual TQWT...
LungAttn: advanced lung sound classification using attention mechanism with dual TQWT and triple STFT spectrogram
Jizuo Li, Jiajun Yuan, Hansong Wang, Shijian Liu, Qianyu Guo, Yi Ma, Yongfu Li, Liebin Zhao, Guoxing Wang
Objective. Auscultation of lung sound plays an important role in the early diagnosis of lung diseases. This work aims to develop an automated adventitious lung sound detection method to reduce the workload of physicians.Approach. We propose a deep learning architecture, LungAttn, which incorporates augmented attention convolution into ResNet block to improve the classification accuracy of lung sound. We adopt a feature extraction method based on dual tunable Q-factor wavelet transform and triple short-time Fourier transform to obtain a multi-channel spectrogram. Mixup method is introduced to augment adventitious lung sound recordings to address the imbalance dataset problem.Main results. Based on the ICBHI 2017 challenge dataset, we implement our framework and compare with the state-of-the-art works. Experimental results show that LungAttn has achieved the Sensitivity, Se, Specificity, Sp and Score of 36.36%, 71.44% and 53.90%, respectively. Of which, our work has improved the Scoreby 1.69% compared to the state-of-the-art models based on the official ICBHI 2017 dataset splitting method.Significance. Multi-channel spectrogram based on different oscillatory behavior of adventitious lung sound provides necessary information of lung sound recordings. Attention mechanism is introduced to lung sound classification methods and has proved to be effective. The proposed LungAttn model can potentially improve the speed and accuracy of lung sound classification in clinical practice.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
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 | ResNet-Att (scratch) | ICBHI Score | 53.90 | #21 of 25 | Archive leaderboard | report |
| Audio Classification | ICBHI Respiratory Sound Database | ResNet-Att (scratch) | Sensitivity | 36.36 | #21 of 25 | Archive leaderboard | report |
| Audio Classification | ICBHI Respiratory Sound Database | ResNet-Att (scratch) | Specificity | 71.44 | #21 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.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections