1. a. Department of Hand Surgery丨 b. Medical Imaging Engineering Center丨 c. Department of Radiology, China-Japan Union Hospital
of Jilin University, Changchun Jilin 130033, China丨 2. HOOKE Instruments Ltd., Changchun Jilin 130033, China丨 3. Changchun
Institute of Optics, Fine Mechanics and Physics, CAS, Changchun Jilin 130033, China
Abstract:Objective The machine learning processing and analysis algorithms for Raman spectral images for rapid biomedical
detection are discussed to provide a reference for the establishment of a convenient and fast innovative detection method for hepatitis
B. Methods Processing and analysis of Raman spectroscopy biomedical assay data using t-SNE clustering algorithm and KNN
classification algorithm. Validation experiments applied Raman spectroscopy to collect Raman spectroscopy data from hepatitis B
infected serum and normal human serum samples, and machine learning algorithms were used to process and analyze the Raman
spectroscopy data to verify the effectiveness of the algorithms on Raman spectroscopy experimental data processing. Results Raman
spectral data processing using t-SNE clustering algorithm and KNN classification algorithm can effectively distinguish hepatitis
B-infected sera from control normal human serum. Conclusion The collection of biomedical sample spectroscopy imaging data
using Raman spectroscopy and processing and analysis by machine learning algorithms such as t-SNE and KNN is a viable new
method for rapid biomedical detection.
基金资助:国家重点研发计划( 2 0 1 8 Y F C 1 3 1 5 6 0 4 丨
2018YFC0116900)丨吉林省科技发展计划项目(20200901017SF)丨
吉林大学高层次科技创新团队建设项目(2017TD-27)
通讯作者:
刘景鑫
E-mail: jingxin@jlu.edu.cn
引用本文:
于铠铭1a,包晓栋1b,李备2,3,洪喜2,刘景鑫1c. 面向生物医学检测的拉曼光谱图像机器学习算法研究[J]. 中国医疗设备, 2021, 36(8): 26-29.
YU Kaiming1a,BAO Xiaodong1b,Li Bei2,3,HONG Xi2,LIU Jingxin1c. Research on Machine Learning Algorithms for Raman Spectroscopy Imaging for Biomedical Detection. China Medical Devices, 2021, 36(8): 26-29.