使用人工神经网络对健康与不健康的诊断进行高精度肺声分类
Weiwei Zhang1, Xinyu Li2, Qiao Liu3
1Infectious Disease Department, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Frontiers in bioengineering and biotechnology
|July 17, 2025
概括
这项研究开发了一个精确的机器学习模型,使用电子耳语镜数据将肺部声音分类为健康或不健康. 该方法实现了高精度,为早期肺部疾病检测提供了有价值的工具.
科学领域:
- 肺部医学 肺部医学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 机器学习和电子耳语镜的进步使得精确的肺声分析能够改善肺部疾病的诊断.
- 精确的肺声分类对于早期检测和呼吸系统疾病的管理至关重要.
研究的目的:
- 开发和评估一种机器学习模型,用于将肺部声音分为健康和不健康的类别.
- 评估音频特征提取和多层感知器 (MLP) 神经网络用于自动肺声分类的有效性.
主要方法:
- 来自112名受试者的肺部声音数据集 (35名健康者,77名肺部疾病患者) 使用3M Littmann®电子听力镜模型3200.收集.
- 提取了五个音频特征 (光谱中位点,功率,能量,零穿越率,MFCCs).
- 一个多层感知器 (MLP) 神经网络被训练用于二进制分类.
主要成果:
- 该MLP模型实现了高分类准确率:在培训组中达到98%,在验证组中达到100%,在测试组中达到94%.
- 该模型在不同数据集中展示了稳定性和准确性.
结论:
- 使用特征提取和MLP神经网络开发的方法是肺声自动二元分类的可靠方法.
- 这种方法作为临床医生有价值的决策支持工具,有助于早期肺部疾病的检测和干预.
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