基于咳声音的深度学习模型用于诊断COVID-19使用统计特征和时间频谱
概括
这项研究开发了一种深度学习模型,使用咳声音识别COVID-19患者. 该模型在多个数据集上实现了高精度,证明了其在非侵入性疾病检测方面的潜力.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 由于COVID-19的流行,人们需要快速且易于使用的诊断工具.
- 咳的声音包含独特的声学特征,可能表明呼吸系统疾病.
研究的目的:
- 开发和评估一种深度学习模型,根据咳声音对COVID-19患者进行分类.
- 评估模型在不同数据集中的通用性.
主要方法:
- 使用Xception预训练模型与可变频复杂解调 (VFCDM) 图像的咳声音.
- 提取的声学特征包括零穿越率 (ZCR),光谱滚动 (SR),光谱中位点 (SC) 和光谱带宽 (SB).
- 将抽取的特征连接到模型的输出层进行分类.
主要成果:
- 在剑桥数据集上获得了0.9346的接收器操作曲线 (AUC) 下的面积.
- 在外部验证数据集上表现出强的性能:0.9244 (Virufy) 和0.8250 (Coswara).
结论:
- 深度学习模型可以有效地使用咳声音分析对COVID-19患者进行分类.
- 拟议的方法显示了为COVID-19的非侵入性,可扩展的诊断工具的承诺.
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