Evaluation of Raman spectra of human brain tumor tissue using the learning vector quantization neural network. (12th April 2016)

• Record Type: Journal Article
• Authors: Liu, Tuo; Chen, Changshui; Shi, Xingzhe; Liu, Chengyong
• Is Part Of: Laser physics. Volume 26:Number 5(2016:May)
• Publication Date: 2016-04-12
• Subjects: Raman spectroscopy -- glioma -- learning vector quantization neural network; Lasers -- Periodicals; 621.36605
• DOI: 10.1088/1054-660X/26/5/055606
• Languages: English
• ISSNs: 1054-660X

Abstract

Abstract: The Raman spectra of tissue of 20 brain tumor patients was recorded using a confocal microlaser Raman spectroscope with 785 nm excitation in vitro . A total of 133 spectra were investigated. Spectra peaks from normal white matter tissue and tumor tissue were analyzed. Algorithms, such as principal component analysis, linear discriminant analysis, and the support vector machine, are commonly used to analyze spectral data. However, in this study, we employed the learning vector quantization (LVQ) neural network, which is typically used for pattern recognition. By applying the proposed method, a normal diagnosis accuracy of 85.7% and a glioma diagnosis accuracy of 89.5% were achieved. The LVQ neural network is a recent approach to excavating Raman spectra information. Moreover, it is fast and convenient, does not require the spectra peak counterpart, and achieves a relatively high accuracy. It can be used in brain tumor prognostics and in helping to optimize the cutting margins of gliomas.