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Dean Hu¹, Zhigang Li¹, Ruixin Wang¹

¹College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|December 19, 2023

概括

一种新的信息平衡 (INB) 功能选择方法使用ATR-FTIR光谱学准确识别COVID-19唾液样本. 将INB与WGAN-GP数据增强相结合,实现了超过90%的准确性,改善了COVID-19分类.

科学领域:

频谱学是一种光谱学.
机器学习机器学习
传染病诊断传染病诊断传染病诊断

背景情况:

由于COVID-19的流行,人们需要快速准确的诊断工具.
减弱总反射里埃变换红外光谱 (ATR-FTIR) 为生物样本分析提供了一种非侵入性方法.
有效的特征选择对于精确分类光谱数据至关重要.

研究的目的:

开发和评估一种新的信息平衡 (INB) 功能选择方法,用于对COVID-19唾液样本进行歧视.
评估INB的性能与使用ATR-FTIR光谱学的传统方法相比.
调查瓦斯斯坦生成对抗网络与梯度惩罚 (WGAN-GP) 数据增强对分类准确性的影响.

主要方法:

提出了信息平衡 (INB) 功能选择方法.
使用瓦斯斯坦生成对抗网络与梯度惩罚 (WGAN-GP) 进行数据增强.
应用INB和WGAN-GP来分析COVID-19唾液样本的减弱总反射里埃变换红外 (ATR-FTIR) 光谱.
将分类准确度与传统特征选择技术进行比较.

主要成果:

对于原始和二次衍生频谱,INB方法比传统方法具有更高的分类准确性.
仅使用INB方法,二级衍生品频谱的分类准确率达到约85%.
结合WGAN-GP数据增强和INB,原始光谱的准确率达到88.7%,二次衍生光谱的准确率达到90.6%.

结论:

拟议的INB方法有效地识别了与COVID-19分类相关的光谱数据特征.
增强WGAN-GP数据可以显著提高用于疾病检测的光谱分析的分类准确性.
这项研究为光谱诊断中的特征选择提供了一个可行的方法,为识别传染病提供了一个新的视角.

关键词:

在ATR-FTIR中使用.在 COVID-19 疫情中,信息平衡功能选择 (INB)在WGAN-GP中使用.

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