|
|
|
|
|
|
Study on Recognition and Classificationof Blood Fluorescence Spectrum with BP Neural Network |
GAO Bin1, ZHAO Peng-fei1, LU Yu-xin1, FAN Ya1, ZHOU Lin-hua1*, QIAN Jun2, LIU Lin-na2, ZHAO Si-yan2, KONG Zhi-feng3 |
1. School of Science, Changchun University of Science and Technology, Changchun 130022, China
2. Changchun Veterinary Institute, Chinese Academic Agricultural Sciences, Changchun 130122, China
3. School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710048, China |
|
|
Abstract There is no doubt that spectrum technology has a positive role in applied prospects of biological and medical testing. Because of the complexity and the similarity ofblood component, study on recognition and classificationof different animal’s blood is still an open issue. Based on the theory of machine learning, by BP neural network, the authorsproposed a methodoffeature extraction and classification for different animal’s blood fluorescence spectra. In this experiment, fluorescence spectra data of whole blood and red blood cell with different concentration (1% and 3%) is collected, respectively. By neighborhood average method, the original data is denoised in order to reduce the impact of noiseon thefeature extraction and classification. For the specialty of blood fluorescence spectra, the authors proposed a new feature extraction method of “Combination and Amplification method”, and established a BP neural network classifier. Compared with other common spectrafeature, “Combination and Amplification”feature and the BP neural network classifiercan achieve good recognition and classification for different animal’s blood fluorescence spectra, and the test error is much less than allowable variation. The technologies in this paper can play an important role inmedical examination, agriculture, and food safety testing.
|
Received: 2017-06-20
Accepted: 2017-10-27
|
|
Corresponding Authors:
ZHOU Lin-hua
E-mail: zhoulh@cust.edu.cn
|
|
[1] Chen Jinjin, Peng Yankun, Li Yongyu, et al. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(14): 1.
[2] Yang Jian, Gong Wei, Shi Shuo, et al. Plant Soil & Environment, 2016, 62(4): 178.
[3] HOU Hua-yi, FANG Zhao-hui, ZHANG Yuan-zhi, et al(候华毅, 方朝晖, 张元志, 等). Chinese Journal of Lasers(中国激光), 2016,(9): 224.
[4] ZHU Xin-jian, HE Xuan, WANG Pin, et al(朱新建, 何 璇, 王 品, 等). Journal of Biomedical Engineering(生物医学工程学), 2016(1): 184.
[5] MEI Lin, CHENG Zheng-xue, SHI Kai-yun, et al(梅 林, 程正学, 石开云, 等). Laser Journal(激光杂志), 2007, 28(3): 84.
[6] ZHANG Zhang, MENG Kun, ZHU Li-guo, et al(张 章, 孟 坤, 朱礼国, 等). Laser Technology(激光技术), 2016, 40(3): 372.
[7] QIN Zong-ding, XU Xue-tang, ZHANG Zhi-zhi, et al(覃宗定, 许雪棠, 张枝芝, 等). Acta Optica Sinica(光学学报), 2014, 34(1): 280.
[8] XU Shao-feng, LIN Shao-zhong, CHEN Rong(许少峰, 林少忠, 陈 荣). Applied Laser(应用激光), 1997,(6): 282.
[9] WAN Xiong, WANG Jian, LIU Peng-xi, et al(万 雄, 王 建, 刘鹏希, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(1):80.
[10] GU Chun-feng, LAN Xiu-feng, YU Yin-shan, et al(顾春峰, 兰秀风, 于银山, 等). Acta Photonica Sinica(光子学报), 2012, 41(1): 107.
[11] MA Cai-hong, CHENG Yu, HE Ming-guang, et al(马彩虹, 程 昱, 何明光, 等). Computer Engineering & Science(计算机工程与科学), 2010, 32(8): 75.
[12] WANG Da-xi, HU Peng(王大溪,胡 鹏). Electronic Science & Technology(电子科技), 2014, 27(8): 11.
[13] YANG Hui-yun, ZHANG You-hui, HUO Li-ling, et al(杨会云, 张有会, 霍利岭, 等). Computer Engineering and Applications(计算机工程与应用), 2010, 46(9): 149.
[14] JI Yu-fang, SUN Yun-qiang, YAO Ai-qin(姬玉芳, 孙运强, 姚爱琴). Electronic Test(电子测试), 2010,(5): 89.
[15] GUO Yang-ming, RAN Cong-bao, JI Xin-yu, et al(郭阳明, 冉从宝, 姬昕禹, 等). Journal of Northwestern Polytechnical University(西北工业大学学报), 2013, 31(1): 44.
[16] GAO Wen, QIAN Ya-guan, WU Chun-ming, et al(高 文, 钱亚冠, 吴春明, 等). Acta Electronica Sinica(电子学报), 2015, 43(4): 795.
[17] Zhou Linhua, Fan Meng, Hou Qiang, et al. Mathematical Biosciences & Engineering, 2018, 15(2): 543. |
[1] |
LEI Hong-jun1, YANG Guang1, PAN Hong-wei1*, WANG Yi-fei1, YI Jun2, WANG Ke-ke2, WANG Guo-hao2, TONG Wen-bin1, SHI Li-li1. Influence of Hydrochemical Ions on Three-Dimensional Fluorescence
Spectrum of Dissolved Organic Matter in the Water Environment
and the Proposed Classification Pretreatment Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 134-140. |
[2] |
GU Yi-lu1, 2,PEI Jing-cheng1, 2*,ZHANG Yu-hui1, 2,YIN Xi-yan1, 2,YU Min-da1, 2, LAI Xiao-jing1, 2. Gemological and Spectral Characterization of Yellowish Green Apatite From Mexico[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 181-187. |
[3] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[4] |
HAN Xue1, 2, LIU Hai1, 2, LIU Jia-wei3, WU Ming-kai1, 2*. Rapid Identification of Inorganic Elements in Understory Soils in
Different Regions of Guizhou Province by X-Ray
Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 225-229. |
[5] |
WANG Hong-jian1, YU Hai-ye1, GAO Shan-yun1, LI Jin-quan1, LIU Guo-hong1, YU Yue1, LI Xiao-kai1, ZHANG Lei1, ZHANG Xin1, LU Ri-feng2, SUI Yuan-yuan1*. A Model for Predicting Early Spot Disease of Maize Based on Fluorescence Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3710-3718. |
[6] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[7] |
SONG Yi-ming1, 2, SHEN Jian1, 2, LIU Chuan-yang1, 2, XIONG Qiu-ran1, 2, CHENG Cheng1, 2, CHAI Yi-di2, WANG Shi-feng2,WU Jing1, 2*. Fluorescence Quantum Yield and Fluorescence Lifetime of Indole, 3-Methylindole and L-Tryptophan[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3758-3762. |
[8] |
YANG Ke-li1, 2, PENG Jiao-yu1, 2, DONG Ya-ping1, 2*, LIU Xin1, 2, LI Wu1, 3, LIU Hai-ning1, 3. Spectroscopic Characterization of Dissolved Organic Matter Isolated From Solar Pond[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3775-3780. |
[9] |
LI Xiao-li1, WANG Yi-min2*, DENG Sai-wen2, WANG Yi-ya2, LI Song2, BAI Jin-feng1. Application of X-Ray Fluorescence Spectrometry in Geological and
Mineral Analysis for 60 Years[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2989-2998. |
[10] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[11] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
[12] |
JIA Yu-ge1, YANG Ming-xing1, 2*, YOU Bo-ya1, YU Ke-ye1. Gemological and Spectroscopic Identification Characteristics of Frozen Jelly-Filled Turquoise and Its Raw Material[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2974-2982. |
[13] |
YANG Xin1, 2, XIA Min1, 2, YE Yin1, 2*, WANG Jing1, 2. Spatiotemporal Distribution Characteristics of Dissolved Organic Matter Spectrum in the Agricultural Watershed of Dianbu River[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2983-2988. |
[14] |
CHEN Wen-jing, XU Nuo, JIAO Zhao-hang, YOU Jia-hua, WANG He, QI Dong-li, FENG Yu*. Study on the Diagnosis of Breast Cancer by Fluorescence Spectrometry Based on Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2407-2412. |
[15] |
ZHU Yan-ping1, CUI Chuan-jin1*, CHENG Peng-fei1, 2, PAN Jin-yan1, SU Hao1, 2, ZHANG Yi1. Measurement of Oil Pollutants by Three-Dimensional Fluorescence
Spectroscopy Combined With BP Neural Network and SWATLD[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2467-2475. |
|
|
|
|