轻量级混合深度学习模型用于从原始肺声音准确分类呼吸系统疾病
Khaldon Lweesy1,2, Sireen Abuqran3, Luay Fraiwan3
1Biomedical Engineering Department, College of Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan. khaldon.lweesy@adu.ac.ae.
Journal of medical systems
|November 28, 2025
概括
这项研究表明,轻量级的深度学习模型可以从原始肺部声音中准确检测呼吸系统疾病. 一种混合CNN-LSTM模型达到高达100%的准确性,证明了改善诊断的潜力.
科学领域:
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
- 呼吸系统医学 呼吸系统医学
背景情况:
- 深度学习显著改善了医学诊断.
- 分析呼吸道病理的原始肺部听觉声音存在挑战.
- 需要有效和准确的呼吸道疾病诊断工具.
研究的目的:
- 评估轻量级的深度学习模型,以使用原始肺耳听声来检测11种呼吸道病理.
- 为了比较不同深度学习模型在原始和增强数据集上的性能.
- 确定最有效的深度学习模型用于呼吸声分析.
主要方法:
- 使用了深度学习框架,使用了多个轻量级模型.
- 在没有特征工程或预处理的情况下,分析了原始肺听觉声音.
- 在原始和增强数据集上评估模型性能.
- 采用混合卷积神经网络 (CNN) - 长期短期记忆 (LSTM) 模型.
主要成果:
- 深度学习模型在从原始肺声音中检测呼吸道病理时取得了显著的准确性 (>99%).
- 混合CNN-LSTM模型的表现优于独立的CNN和LSTM模型.
- 数据增强提高了深度学习模型的性能.
- 轻量级混合CNN-LSTM模型在增强数据集上达到100%的准确性.
结论:
- 通过轻量级的深度学习模型,可以可靠地使用原始肺听觉声音来检测多种呼吸道病理.
- 混合CNN-LSTM模型为呼吸系统疾病诊断提供了高效和准确的解决方案.
- 需要进一步的外部验证来评估跨不同临床数据集的模型概括性.
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