LungAttn: advanced lung sound classification using attention mechanism with dual TQWT and triple STFT spectrogram.

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 (TQWT) and triple short-time Fourier transform (STFT) 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 Score by 1.69% compared to the state-of-the-art models based on 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.