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In Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

According to the limited molecular information reflected by single spectroscopy, and the complementarity of FTIR spectroscopy and Raman spectroscopy, we propose a novel diagnostic technology combining multispectral fusion and deep learning. We used serum samples from 45 healthy controls, 44 non-small cell lung cancer (NSCLC), 38 glioma and 37 esophageal cancer patients, and the Raman spectra and FTIR spectra were collected respectively. Then we performed low-level fusion and feature fusion on the spectral, and used SVM, Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM) and the multi-scale convolutional fusion neural network (MFCNN). The accuracy of low-level fusion and feature fusion models are improved by about 10% compared with single spectral models.

Leng Hongyong, Chen Cheng, Chen Chen, Chen Fangfang, Du Zijun, Chen Jiajia, Yang Bo, Zuo Enguang, Xiao Meng, Lv Xiaoyi, Liu Pei


FTIR spectroscopy, Feature fusion, Low-level fusion, MFCNN, Raman