对早期喘查咳声音的分类研究
Yanming Huo1, Jiajing Ma2, Huixian Liu2
1School of Electrical Engineering, Hebei University of Science and Technology, No. 26 Yuxiang Street, Yuhua District, Shijiazhuang, Hebei, China. 1213101415@qq.com.
Current allergy and asthma reports
|August 23, 2025
概括
这项研究引入了一种新的深度学习框架, 多模型方法实现了95.9%的准确性,显示了可扩展,非侵入性临床查的潜力.
科学领域:
- 医学的人工智能
- 呼吸系统医学
- 医疗保健中的机器学习
背景情况:
- 深度学习模型显示了通过呼吸声分析检测肺部疾病的前景.
- 现有研究经常使用单一网络架构,忽视阶级不平衡和培训稳定性,限制了现实世界的适用性.
研究的目的:
- 评估使用咳声音进行有效和可扩展的早期喘检测的多模型深度学习融合框架.
- 评估该框架在临床环境中提高诊断准确性和通用性的潜力.
主要方法:
- 通过融合层集成ResNet18,VGG16和DenseNet121的喘检测模型.
- 使用SMOTE (合成少数采样技术) 来解决数据不平衡.
- 使用加权交叉损失,混合精度训练和StepLR调度以提高强度和性能.
主要成果:
- 拟议的融合模型在试验组中达到95.9%的准确性.
- 具有强大的概括能力,表明适合于不同的临床环境.
- 综合方法解决了单一网络架构的局限性,并提高了培训稳定性.
结论:
- 多模型深度学习融合框架为早期喘检测提供了一个有前途的方法.
- 该模型的高精度和通用性支持其实时,非侵入性查的潜力.
- 这种方法促进了人工智能在呼吸诊断中的应用.
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