RAMAN SPECTROSCOPY FOR BREAST CANCER DETECTION VIA PRINCIPAL COMPONENT ANALYSIS

本研究的目的是将拉曼光谱与 组织病理学使用主成分分析。 主要 成分分析使我们能够将数据集缩减为更小的 一些数学线形，称为主成分， 表示数据中的光谱变化。 的线性组合 然后，这些主成分可以用来拟合单个数据。 最后用logisitic回归进行相关拟合 系数与疾病分类，并制定一个诊断 算法 到目前为止，我们已经开发了一个初始决策算法， 并正在测试它的数据不包括在 原始数据集。

The goal of this research is to correlate Raman spectra with tissue pathology using principal component analysis. Principal component analysis enables us to reduce our data set into a smaller number of mathematical lineshapes, called principal components, which represent the spectral variations in the data. A linear combination of these principal components can then be used to fit individual data. Finally, logisitic regression is used to correlate fitting coefficients with disease classifications and develop a diagnostic algorithm. So far, we have developed an initial decision algorithm and are in the process of testing it on data not included in the original data set.