Raman spectroscopy combined with convolutional neural network for the sub-types classification of breast cancer and critical feature visualization.

Journal: Computer methods and programs in biomedicine
PMID: 39116820

Abstract

PROBLEMS: Raman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the current Raman prediction models fail to cover all the molecular sub-types of breast cancer, and lack the visualization of the model.

Authors

  • Juan Li
    Department of Hygienic Inspection, School of Public Health, Jilin University 1163 Xinmin Street Changchun 130021 Jilin China songxiuling@jlu.edu.cn li_juan@jlu.edu.cn jinmh@jlu.edu.cn +86 43185619441.
  • Xiaoting Wang
    He University, Shenyang, 110000, China.
  • Shungeng Min
    College of Science, China Agricultural University, Beijing 100193, PR China. Electronic address: minsg@cau.edu.cn.
  • Jingjing Xia
    College of Science, China Agricultural University, Beijing 100193, PR China.
  • Jinyao Li
    School of Pharmaceutical Sciences and Institute of Materia Medica & Xinjiang Key Laboratory of Biological Resources and Genetic Engineering, College of Life Science and Technology, Xinjiang University, Urumqi, 830017, China.

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