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CovidCoughNet:一种基于卷积神经网络和深度特征提取的新方法,使用调度转移数据增强用于从咳,呼吸和语音信号检测covid-19

Gaffari Celik¹

¹Agri Ibrahim Cecen University, Department of Computer Technology, Agri, Turkey.

Computers in biology and medicine

|June 15, 2023

概括

一个新的深度学习模型,CovidCoughNet,通过咳,呼吸和语音信号准确检测Covid-19. 这种方法使用先进的特征提取和数据增强技术显著提高了检测准确性.

科学领域:

人工智能的人工智能
生物医学信号处理
传染病诊断传染病诊断传染病诊断

背景情况:

早期和准确检测Covid-19对于疾病管理和控制至关重要.
分析咳,呼吸和声音等非侵入性生物信号为远程COVID-19查提供了一个有希望的途径.

研究的目的:

开发和评估一种基于深度学习的方法,CovidCoughNet,用于使用咳,呼吸和语音信号高性能检测Covid-19.
研究新型深度特征提取和数据增强技术在提高诊断准确性的有效性.

主要方法:

拟议的CovidCoughNet模型集成InceptionFireNet用于深度特征提取和DeepConvNet用于预测.
使用了COUGHVID和Coswara数据集,包括咳,呼吸和语音记录.
应用了调度转移来增强数据,并使用了各种信号处理技术 (CF,RMSE,SC,SB,SR,ZCR,MFCC) 来提取特征.

主要成果:

与原始信号相比,调度转移技术提高了大约3%的性能.
在COUGHVID数据集 (99.19%准确率) 和Coswara语音数据 (99.63%准确率) 上,CovidCoughNet实现了高性能.
该模型与现有的Covid-19检测文献方法相比,表现优异.

结论:

CovidCoughNet提供了一个高度准确和有效的深度学习解决方案,用于从呼吸道和语音信号检测Covid-19.
该研究强调了人工智能驱动的生物信号分析对于可扩展和非侵入性疾病查的潜力.
开发的方法和开源代码 (可在GitHub上找到) 可以促进进一步的研究和临床应用.

关键词:

卷积神经网络是一种卷积神经网络.咳的声音,就是咳的声音.这就是Covid-19的原因.深度学习是一种深度学习.梅尔的频率是塞普斯特拉尔系数.转移球场的转移.声音分析声音分析语音信号的声音信号.

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