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Academic Journal of Engineering and Technology Science, 2022, 5(11); doi: 10.25236/AJETS.2022.051110.

Lung Sound Recognition Based on Pre-Trained Convolutional Neural Network


Shanshu Bao1, Lei Liu2, Bo Che2, Linhong Deng2

Corresponding Author:
Shanshu Bao

1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, 213164, China

2Changzhou Key Laboratory of Respiratory Medical, Engineering, Institute of Biomedical Engineering and Health Sciences, Changzhou University, Changzhou, Jiangsu, 213164, China


It has become one of the focuses of respiratory medicine to recognize lung sounds by machine learning methods, and then assist doctors to diagnose patients' pulmonary diseases. Aiming at the problems of model overfitting and low classification accuracy caused by the small size of lung sound dataset in current lung sound recognition, a lung sound recognition method based on the combination of pre-trained convolutional neural network and CatBoost algorithm was proposed. The pre-trained convolutional neural network on the image dataset ImageNet is transferred to lung sound recognition. The channel attention mechanism CBAM is fused to enhance the recognition performance of the network, and the lung sound waveform data is converted into logarithmic MEL frequency spectrum for input and training. Finally, the trained model is used as a feature extractor, and the feature vectors with high-level semantics are input into the ensemble learning algorithm CatBoost to achieve the final classification. After experiment, the result shows that the specificity, sensitivity and ICHBI-score of the proposed method for lung sound recognition in ICBHI-2017 lung sound dataset reach 88.34%, 63.13% and 75.73%, respectively, which is superior to the previous methods. The display has a good application prospect in lung sound recognition.


lung sound recognition; convolutional neural network; channel attention; ensemble learning

Cite This Paper

Shanshu Bao, Lei Liu, Bo Che, Linhong Deng. Lung Sound Recognition Based on Pre-Trained Convolutional Neural Network. Academic Journal of Engineering and Technology Science (2022) Vol. 5, Issue 11: 70-76. https://doi.org/10.25236/AJETS.2022.051110.


[1] Rocha B M, Filos D, Mendes L, et al. Α respiratory sound database for the development of automated classification[C].International Conference on Biomedical and Health Informatics. Springer, Singapore, 2017: 33-37.

[2] Serbes G, Ulukaya S, Kahya Y P. An automated lung sound preprocessing and classification system based onspectral analysis methods[C].International Conference on Biomedical and Health Informatics. Springer, Singapore, 2017: 45-49.

[3] Demir F, Ismael A M, Sengur A. Classification of lung sounds with CNN model using parallel pooling structure [J]. IEEE Access, 2020, 8: 105376-105383.

[4] Ma Y, Xu X, Yu Q, et al. LungBRN: A smart digital stethoscope for detecting respiratory disease using bi-resnet deep learning algorithm[C].2019 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2019: 1-4.

[5] Prokhorenkova L, Gusev G, Vorobev A, et al. CatBoost: unbiased boosting with categorical features [J]. Advances in Neural Information Processing Systems, 2018, 31.

[6] Woo S, Park J, Lee J Y, et al. Cbam: Convolutional block attention module[C].Proceedings of the European Conference on Computer Vision (ECCV). 2018: 3-19.

[7] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 7132-7141.

[8] Wang Q, Wu B et al. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks [C].2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ:IEEE Press, 2020.

[9] Chen T, Guestrin C. Xgboost: A scalable tree boosting system[C].Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016: 785-794.

[10] Ke G, Meng Q, Finley T, et al. Lightgbm: A highly efficient gradient boosting decision tree [J]. Advances in neural information processing systems, 2017, 30.

[11] Demir F, Sengur A, Bajaj V. Convolutional neural networks based efficient approach for classification of lung diseases [J]. Health Information Science and Systems, 2020, 8(1): 1-8.

[12] Gairola S, Tom F, Kwatra N, et al. Respirenet: A deep neural network for accurately detecting abnormal lung sounds in limited data setting[C].2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021: 527-530.