RAMAN MICROSPECTROSCOPY FOR BREAST CANCER DETECTION

这项研究的目标是将拉曼光谱与 组织病理学采用主成分分析。本金 组件分析使我们能够将数据集缩减为更小的 数学线形的数量，称为主分量，它 表示数据中的光谱变化。线性组合 然后可以使用这些主成分来拟合单个数据。 最后，用Logsitic回归进行相关拟合 与疾病分类相关的系数并开发诊断 算法。到目前为止，我们已经开发了一个初始决策算法 并正在使用未包括在 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.