[1]肖欣,高玉清,张建敏.基于原始肺音的端到端深度学习肺炎辅助诊断[J].中国医学物理学杂志,2025,42(2):274-280.[doi:DOI:10.3969/j.issn.1005-202X.2025.02.021]
　XIAO Xin,GAO Yuqing,ZHANG Jianmin.End-to-end deep learning for auxiliary diagnosis of pneumonia using original lung sounds[J].Chinese Journal of Medical Physics,2025,42(2):274-280.[doi:DOI:10.3969/j.issn.1005-202X.2025.02.021]
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基于原始肺音的端到端深度学习肺炎辅助诊断()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第2期
页码:
274-280
栏目:
医学人工智能
出版日期:
2025-01-20

文章信息/Info

Title:
End-to-end deep learning for auxiliary diagnosis of pneumonia using original lung sounds
文章编号:
1005-202X（2025）02-0274-07
作者:
肖欣; 高玉清; 张建敏
江汉大学人工智能学院， 湖北 武汉 430056
Author(s):
XIAO Xin;  GAO Yuqing;  ZHANG Jianmin
School of Artificial Intelligence, Jianghan University, Wuhan 430056, China
关键词:
原始肺音; 肺炎; 智能辅助诊断; 端到端学习; DCL-Net
Keywords:
Keywords: original lung sound pneumonia intelligent auxiliary diagnosis end-to-end learning DCL-Net
分类号:
R318；R563.1
DOI:
DOI:10.3969/j.issn.1005-202X.2025.02.021
文献标志码:
A
摘要:
提出一种基于双路径不同卷积核的DCL-Net的端到端肺炎辅助诊断方法。该方法无需进行特征工程，将原始肺音信号直接输入模型，利用卷积核分别为1[?3和1[?5的双路径卷积网络，每个路径包含3个残差块，以便模型自动学习肺音信号不同尺度的特征，同时避免模型退化问题。为验证端到端方法的性能，将其与信号分析领域常用的梅尔倒谱图、短时傅里叶变换和小波变换这3种特征提取方法进行比较。结果显示，四分类任务（正常、普通、病重、病危）诊断准确率为61.4%，相比3种特征工程方法分别提高1.6%、5.0%和3.7%；二分类任务（正常、异常）诊断准确率为89.7%，相比3种特征工程方法分别提高11.0%、5.1%和11.2%。实验结果表明该方法可为肺炎病情评估提供更有效的诊断工具。
Abstract:
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.

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备注/Memo

备注/Memo:
【收稿日期】2024-10-26 【基金项目】武汉市科技计划项目（2020020601012320）；老年医学与现代康养学科（群）项目（［鄂教研函［2021］5号］） 【作者简介】肖欣，硕士研究生，研究方向：智慧医疗和人工智能应用，E-mail: bellaxiao129@163.com 【通信作者】张建敏，博士，教授，研究方向：智慧医疗和人工智能应用，E-mail: zhangjm@jhun.edu.cn
更新日期/Last Update: 2025-01-22