Rapid screening of hepatitis B using Raman spectroscopy and long short-term memory neural network.

Journal: Lasers in medical science
Published Date: Oct 1, 2020

Abstract

This study presents a rapid method to screen hepatitis B patients using serum Raman spectroscopy combined with long short-term memory neural network (LSTM). The serum samples taken from 435 hepatitis B patients and 699 non-hepatitis B people were measured in this experiment. Specific biomolecular changes in three groups of serum samples could be seen in the tentative assignment of Raman peaks. First, principal component analysis (PCA) was used for extracting key features of spectral data, which reduces the dimension of the multidimensional spectrum. Then, LSTM is used to train the spectral data. Finally, the full connection layer completes the classification of HBV. The diagnostic accuracy of the first LSTM model is 97.32%, and the value of AUC is 0.995. The results from the study demonstrate that the combination of serum Raman spectroscopy technique and LSTM provides an effective technical approach to the screening of hepatitis B.

Authors

  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Shengwei Tian
    College of Software Engineering, Xin Jiang University, Urumuqi, 830000, China.
  • Long Yu
    Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China. Electronic address: yulong@dicp.ac.cn.
  • Xiaoyi Lv
    College of Information Science and Engineering, Xinjiang University, Urumqi, China.
  • Zhaoxia Zhang
    Department of Laboratory Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumuqi, 830000, China.

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