6 February 2013 Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection
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Abstract
The ability of combining serum surface-enhanced Raman spectroscopy (SERS) with support vector machine (SVM) for improving classification esophageal cancer patients from normal volunteers is investigated. Two groups of serum SERS spectra based on silver nanoparticles (AgNPs) are obtained: one group from patients with pathologically confirmed esophageal cancer (n=30 ) and the other group from healthy volunteers (n=31 ). Principal components analysis (PCA), conventional SVM (C-SVM) and conventional SVM combination with PCA (PCA-SVM) methods are implemented to classify the same spectral dataset. Results show that a diagnostic accuracy of 77.0% is acquired for PCA technique, while diagnostic accuracies of 83.6% and 85.2% are obtained for C-SVM and PCA-SVM methods based on radial basis functions (RBF) models. The results prove that RBF SVM models are superior to PCA algorithm in classification serum SERS spectra. The study demonstrates that serum SERS in combination with SVM technique has great potential to provide an effective and accurate diagnostic schema for noninvasive detection of esophageal cancer.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)
Shao-Xin Li, Shao-Xin Li, Qiu-Yao Zeng, Qiu-Yao Zeng, Lin-Fang Li, Lin-Fang Li, Yan-Jiao Zhang, Yan-Jiao Zhang, Ming-Ming Wan, Ming-Ming Wan, Zhi-Ming Liu, Zhi-Ming Liu, Hong-Lian Xiong, Hong-Lian Xiong, Zhou-Yi Guo, Zhou-Yi Guo, Song-Hao Liu, Song-Hao Liu, } "Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection," Journal of Biomedical Optics 18(2), 027008 (6 February 2013). https://doi.org/10.1117/1.JBO.18.2.027008 . Submission:
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