深度学习驱动的呼吸系统疾病的早期诊断,使用肺声数据上的CNN-RNN融合
Thulasi Bikku1, Satya Sree K P N V2, Srinivasarao Thota3
1Department of Computer Science and Engineering, Amrita School of Computing Amaravati, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, 522503, India. b_thulasi@av.amrita.edu.
Scientific reports
|November 25, 2025
概括
这项研究介绍了用于肺声音分析的深度学习算法,有效检测肺炎,喘和COPD等呼吸道疾病. 该模型实现了高精度,为早期疾病诊断提供了有前途的工具.
科学领域:
- * 医学信息学 医学信息学
- * 医疗保健中的人工智能
- * 呼吸系统医学 呼吸系统医学
背景情况:
- * 准确和早期发现呼吸道疾病仍然是一个重大的临床挑战.
- * 传统的诊断方法可能是侵入性的或耗时的.
- * 肺声分析提供了一种非侵入性的呼吸评估方法.
研究的目的:
- * 开发和评估用于自动化肺声分析的深度学习算法.
- * 改善早期检测和区分各种呼吸道疾病.
- *通过可解释的人工智能增强诊断准确性和临床信任.
主要方法:
- * 一种混合深度学习模型,结合了卷积神经网络 (CNN) 和循环神经网络 (RNN) 架构.
- * 在高通过和细分分析肺声录制后,利用Mel光谱作为输入特征.
- * 在训练中使用数据增强技术和具有交叉损失的Adam优化器.
主要成果:
- *该算法在ICBHI数据集上的多种呼吸道疾病中显示出高准确性:93.3% (健康),93.8% (肺炎),91.7% (喘) 和94.0% (COPD).
- *在ICBHI和Coswara数据集上,与传统的机器学习算法 (决策树,SVM,随机森林) 相比,在精度,回忆,F1得分和准确性方面取得了卓越的性能.
- * 该模型提供可解释的视觉解释 (Grad-CAM) 和信心估计,增强临床效用.
结论:
- *开发的深度学习算法是通过肺声分析来诊断呼吸系统疾病的高度准确和有效工具.
- * 混合CNN-RNN模型显示了早期疾病检测和区分的巨大潜力,优于现有方法.
- * 包含可解释性特征增强了临床信任,并促进了AI在呼吸道诊断中的采用.
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