中国计量大学学报2023，Vol.34Issue(4)：533-540,8.DOI:10.3969/j.issn.2096-2835.2023.04.006
基于血清SERS光谱与特征波长提取的肺癌识别方法
A classification method of lung cancer based on SERS spectra of human serum and feature wavelength extraction
摘要
Abstract
Aims:This paper aims to improve the stability and classification accuracy of serum surface enhanced Raman spectroscopy(SERS)classification models for lung cancer patients and healthy individuals.Methods:Recursive feature selection,the successive projections algorithm,the competitive adaptive reweighted sampling algorithm and principal component analysis were used to select the spectral features.Then the deep neural network,partial least squares discriminant analysis,and the support vector machine classification algorithm were used to establish a Raman spectral classification model for lung cancer serum.Results:Recursive feature selection and the competitive adaptive reweighting algorithm had significant effects on improving the stability of the classification models.For the competitive adaptive reweighting algorithm,the training set cross-validation accuracy of the deep neural network classification models was 94.55％.The test set accuracy was 93.75％.The sensitivity was 87.5％;and the specificity was 100％,which was superior to the other two models.Conclusions:A classification model based on feature wavelength extraction was established to effectively identify SERS spectra from lung cancer patients and healthy individuals.关键词
肺癌筛查/表面增强拉曼光谱/光谱特征提取/分类算法Key words
lung cancer screening/surface-enhanced Raman spectroscopy/spectral feature extraction/classification algorithm分类
数理科学引用本文复制引用
王子林,金尚忠,窦婷婷..基于血清SERS光谱与特征波长提取的肺癌识别方法[J].中国计量大学学报,2023,34(4):533-540,8.基金项目
浙江省自然科学基金项目(No.LZ22F050004),浙江省省级重点研发计划项目(No.2020C03095) （No.LZ22F050004）
