基于积累的双光谱图像的呼吸声信号分类,使用深度学习.
Sandeep B Sangle1, Chandrakant J Gaikwad1
1Department of Electronics and Telecommunication Engineering, Ramrao Adik Institute of Technology, D.Y. Patil Deemed to be University, Nerul, Navi Mumbai, Maharashtra 400706 India.
概括
这项研究引入了一种新的方法,使用累积的双光谱特征和人工智能算法来从呼吸声中检测COVID-19. 该方法在区分健康和COVID-19感染个体方面显示出有希望的准确性.
科学领域:
- 医疗信号处理 医疗信号处理
- 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:
- 由于COVID-19的流行,需要快速且易于使用的诊断工具.
- 呼吸道声分析提供了一种非侵入性方法来检测疾病.
- 新出现的SARS-CoV-2变种凸显了持续监测和诊断的必要性.
研究的目的:
- 开发和评估一种使用呼吸声检测COVID-19的新方法.
- 分析累积的双谱特征在分类呼吸声中的有效性.
- 为了比较卷积神经网络 (CNN) 和ResNet-50算法的COVID-19分类性能.
主要方法:
- 用从主要域双光谱中获得的新型积累的双光谱特征分析呼吸声.
- 双光谱图像是从大小双光谱生成的.
- 卷积神经网络 (CNN) 和ResNet-50模型被设计和训练来将声音分类为COVID-19阳性或健康.
- 性能与现有的最先进的方法进行了基准测试.
主要成果:
- 提出的基于CNN的方法实现了87.68%的高精度,用于浅呼吸的声音.
- 对于深呼吸的声音,ResNet-50的高准确率为87.62%.
- 与基线方法相比,实施的方法在各种类型的呼吸声中表现得更好.
结论:
- 累积的双光谱特征与CNN和ResNet-50相结合,为非侵入性COVID-19检测提供了一种可行的方法.
- 该研究强调了人工智能驱动的呼吸声分析在公共卫生查中的潜力.
- 进一步的研究可以探索更广泛的应用,并改进算法,以提高诊断准确度.
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