End-to-end deep learning for auxiliary diagnosis of pneumonia using original lung sounds
10.3969/j.issn.1005-202X.2025.02.021
- VernacularTitle:基于原始肺音的端到端深度学习肺炎辅助诊断
- Author:
Xin XIAO
1
;
Yuqing GAO
1
;
Jianmin ZHANG
1
Author Information
1. 江汉大学人工智能学院,湖北 武汉 430056
- Publication Type:Journal Article
- Keywords:
original lung sound;
pneumonia;
intelligent auxiliary diagnosis;
end-to-end learning;
DCL-Net
- From:
Chinese Journal of Medical Physics
2025;42(2):274-280
- CountryChina
- Language:Chinese
-
Abstract:
An end-to-end auxiliary diagnosis method for pneumonia based on DCL-Net with dual-path of different convolutional kernels is proposed,in which no feature engineering is required,and the original lung sound signal is directly input into the model.The dual-path convolutional network with kernel sizes of 1*3 and 1*5,with each path containing 3 residual blocks,allows the model to automatically learn features of lung sounds at different scales while avoiding model degradation.The performance of the end-to-end method is validated through the comparisons with 3 commonly used feature extraction methods in signal analysis,namely Mel-spectrogram,short-time Fourier transform,and wavelet transform.The results show that the proposed method has a diagnostic accuracy of 61.4%for the 4-class classification task(normal,moderate,severe,critical),which is 1.6%,5.0%,and 3.7%higher than the other 3 feature extraction methods,and the diagnostic accuracy is 89.7%for the binary classification task(normal or abnormal),which is 11.0%,5.1%,and 11.2%higher than the other 3 feature engineering methods,demonstrating that it can serve as an effective diagnostic tool for pneumonia.
