自动诊断COVID-19相关的呼吸道疾病从语音自动诊断
Kushan Shekhar1, Nagaratna B Chittaragi2, Shashidhar G Koolagudi1
1Department of CSE, National Institute of Technology Karnataka, Surathkal, Mangalore, Karnataka India.
概括
这项研究开发了一种人工智能系统,使用语音处理来检测咳和呼吸声音的COVID-19. 最好的模型达到84%的准确性,帮助医疗保健工作人员进行诊断.
科学领域:
- 人工智能的人工智能
- 语音处理 语音处理
- 医学诊断 医学诊断 医学诊断
背景情况:
- COVID-19的诊断依赖于临床评估和测试.
- 需要使用非侵入性方法的客观诊断工具.
研究的目的:
- 开发一种智能系统,从语音中自动识别COVID-19疾病.
- 评估音频功能和机器学习分类器对此任务的有效性.
主要方法:
- 利用了一组来自众人的咳和呼吸声音数据集.
- 提取了标准的音频特征 (光谱,声) 和激发源特征.
- 训练有素的浅层分类器,包括集体方法和支持向量机 (SVM).
主要成果:
- 该系统在咳和呼吸数据集上表现良好.
- 使用标准音频功能与结合咳和呼吸数据,达到84%的最高准确率和84%的曲线下面面积 (AUC).
- 最佳的特征和分类器组合在数据集之间有所不同.
结论:
- 自动语音处理显示了COVID-19检测的前景.
- 这种人工智能驱动的方法可以帮助医疗保健专业人员进行早期诊断和医疗系统支持.
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