Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method.

Journal: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Published Date: Sep 20, 2022

Abstract

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.

Authors

  • Hongyong Leng
    School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China; College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China.
  • Cheng Chen
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.
  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Fangfang Chen
    College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China.
  • Zijun Du
    University of Macau, Macao Special Administrative Region, 999078, China.
  • Jiajia Chen
    Zhongshan Hospital Xiamen University, Xiamen, Fujian 361004, China.
  • Bo Yang
    Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China.
  • Enguang Zuo
    College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
  • Meng Xiao
    Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China.
  • Xiaoyi Lv
    College of Information Science and Engineering, Xinjiang University, Urumqi, China.
  • Pei Liu
    School of Life Sciences, Nanjing University, State Key Laboratory of Pharmaceutical Biotechnology, Nanjing 210000, China.

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