Abstract-Dimensionality Reduction for Identification of Hepatic Tumor Samples Based on Terahertz Time-Domain Spectroscopy

 Haishun Liu,  Zhenwei Zhang, Xin Zhang,  Yuping Yang, Zhuoyong Zhang, Xiangyi Liu, Fan Wang,  Yiding Han, Cunlin Zhang

https://ieeexplore.ieee.org/document/8333778/

Terahertz time-domain spectroscopy (THz-TDS) combining with chemometrics methods was proposed for the identification of hepatic tumors. Two linear compression methods, principle component analysis and locality preserving projections (LPPs), and a nonlinear method, Isomap, were used to reduce the dimensionality of the measured dataset. Comparing two-dimensional (2-D) data reduced by these three dimensionality reduction techniques, only 2-D Isomap plot could separate the distances between two classes for the THz time-domain data and LPP had capacity of distinguishing two types of samples building on frequency-domain data. The best classification accuracies from 2-D time-domain data were 99.81±0.30% and 99.69±0.61% given by Isomap probabilistic neural network (PNN) and Isomap support vector machine (SVM), respectively, while the best classification results of 2-D frequency-domain data were 100.00±0.00%, 99.75±0.32% provided by LPP-PNN, LPP-SVM. The results showed that Isomap and LPP are appropriate techniques to reflect the nonlinear manifold of the THz data. The THz technology either in time-domain or frequency-domain coupled with Isomap-PNN or LPP-PNN could offer a potential procedure to identify hepatic tumors.

Abstract-Terahertz time-domain spectroscopy combined with support vector machines and partial least squares-discriminant analysis applied to diagnosis of cervical carcinoma

Na Qi,   Zhuoyong Zhang,   Yuhong Xiang,   Yuping Yang,  Xueai Liang and   Peter B Harrington  

Anal. Methods, 2015, Accepted Manuscript

DOI: 10.1039/C4AY02665A
Received 09 Nov 2014, Accepted 20 Jan 2015
First published online 20 Jan 2015
http://pubs.rsc.org/en/Content/ArticleLanding/2015/AY/C4AY02665A?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+rss%2FAY+%28RSC+-+Anal.+Methods+latest+articles%29#!divAbstract

Coupled with terahertz time-domain spectroscopy (THz-TDS) technology, the feasibility of diagnosis of cervical carcinoma using support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA) had been studied. The terahertz spectra of 52 specimens of cervix were collected. The performance of preprocessing methods of multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing and first derivative, principal component orthogonal signal correction (PC-OSC) and emphatic orthogonal signal correction (EOSC) were investigated for PLS-DA and SVM models, respectively. The effects of the different pretreatments methods with respect to classification accuracy were compared. The PLS-DA and SVM models were validated using the bootstrapped Latin-partition method. The SVM and PLS-DA models optimized with the combination of SG first derivative and PC-OSC preprocessing had the best predictive results with classification rates of 94.0 ± 0.4% and 94.0 ± 0.5%, respectively. The proposed procedure proved that terahertz spectroscopy combined with classifiers provides a technology which has potential as a new diagnosis method for cancer tissue.