基于深度学习的咳分类,使用应用程序记录的声音:与VGGishish的转移学习方法
Sanghoon Han1, Yu-Rim Lee1, Ji-Ho Lee2
1Waycen Inc, Seoul, 06167, Republic of Korea.
BMC medical informatics and decision making
|July 2, 2025
概括
这项研究开发了一种深度学习模型,使用智能手机记录来检测异常咳. 该模型有助于早期诊断呼吸道疾病,改善患者和临床医生的可访问性和准确性.
科学领域:
- 生物声学和医学信息学
- 医疗保健中的人工智能
- 呼吸道疾病诊断 呼吸道疾病诊断
背景情况:
- 咳的声音包含重要的生物识别信息,用于评估呼吸系统疾病.
- 由于并发症和死亡率高,早期诊断呼吸道疾病至关重要.
- 建议采用深度学习方法来增强早期诊断和改善患者的治疗结果.
研究的目的:
- 开发和评估一种深度学习模型,用于使用咳声音早期诊断呼吸系统疾病.
- 评估临床专业知识和诊断输入对模型概括的影响.
- 为临床医生和医疗保健服务有限的个人提供一个工具.
主要方法:
- 一个使用VGGish进行转移学习的深度学习框架,包含检测和分类网络.
- 咳事件通过检测模型被确定,并通过第二个模型被归类为正常或异常.
- 模型接受了智能手机录制的咳声音的训练,由多名医学专家精心标记.
主要成果:
- 咳检测模型实现了高精度 (0.9883).
- 咳分类模型在三个数据集 (0.8417-0.8662) 中表现出强的性能.
- 用Grad-CAM进行功能可视化,并使用AUROC和AUPRC验证性能.
结论:
- 拟议的咳分类模型可以支持获得有限医疗保健的个人和经验较少的医疗专业人员.
- 这种深度学习方法,利用智能手机记录的咳,有助于更早地检测和管理呼吸系统疾病.
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