Xun Chen, Jianghao Shen, Chang Liu, Xiaoyu Shi, Weichen Feng, Hongyi Sun, Weifeng Zhang, Shengpai Zhang, Yuqing Jiao, Jing Chen, Kun Hao, Qi Gao, Yitong Li, Weili Hong, Pu Wang, Limin Feng, Shuhua Yue
Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i...
April 11, 2024: Analytical Chemistry