Dimensionality Reduction for Identification of Hepatic Tumor Samples Based on Terahertz Time-Domain Spectroscopy
Dimensionality Reduction for Identification of Hepatic Tumor Samples Based on Terahertz Time-Domain Spectroscopy
复制标题
基于太赫兹时域光谱的肝脏肿瘤样本降维鉴定
DOI：
10.1109/tthz.2018.2813085
复制
发表时间：
2018-05-01
影响因子：
3.2
通讯作者：
Zhang, Cunlin
中科院分区：
文献类型：
--
作者：
Liu, Haishun;Zhang, Zhenwei;Zhang, Cunlin
提出了将太赫兹时域光谱（THz-TDS）与化学计量学相结合的方法用于肝脏肿瘤的鉴别。采用主成分分析和局域保持投影（LPPs）两种线性压缩方法，以及一种非线性方法Isomap对测量数据集进行降维。对比这三种降维方法的二维（2-D）数据，太赫兹时域数据只有2-D等高线图可以区分两类之间的距离，而LPP基于频域数据具有区分两类样本的能力。Isomap概率神经网络（PNN）和Isomap支持向量机（SVM）对二维时域数据的最佳分类精度分别为99.81 +/- 0.30%和99.69 +/- 0.61%，而LPP-PNN和LPP-SVM对二维频域数据的最佳分类精度分别为100.00 +/- 0.00%和99.75 +/- 0.32%。结果表明，Isomap和LPP是反映太赫兹数据非线性流形的合适技术。时域或频域太赫兹技术与Isomap-PNN或LPP-PNN相结合，可以为肝脏肿瘤的识别提供一种潜在的方法。
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.