A repository & source of cutting edge news about emerging terahertz technology, it's commercialization & innovations in THz devices, quality & process control, medical diagnostics, security, astronomy, communications, applications in graphene, metamaterials, CMOS, compressive sensing, 3d printing, and the Internet of Nanothings. NOTHING POSTED IS INVESTMENT ADVICE! REPOSTED COPYRIGHT IS FOR EDUCATIONAL USE.
Monday, April 30, 2018
Abstract-Dimensionality Reduction for Identification of Hepatic Tumor Samples Based on Terahertz Time-Domain Spectroscopy
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.