|
|
|
|
|
|
Classification of Mycoplasma Pneumoniae Strains Based on
One-Dimensional Convolutional Neural Network and
Raman Spectroscopy |
ZHAO Yong1, HE Men-yuan1, WANG Bo-lin2, ZHAO Rong2, MENG Zong1* |
1. School of Electrical Engineering, Yanshan University, The Key Laboratory of Measurement Technology and Instruments of Hebei Province, Qinhuangdao 066004, China
2. School of Information Science and Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China
|
|
|
Abstract Mycoplasma pneumoniae is the main cause of human respiratory diseases. Clinically, the symptoms of patients infected with different mycoplasma pneumoniae are very similar, so it is difficult to distinguish the type of mycoplasma pneumoniae according to the symptoms and give medication. Therefore, the accurate identification of mycoplasma pneumoniae strain type is of great significance for the pathogenesis and epidemiological research of the disease, and accurate clinical treatment. Raman spectrum has been paid more and more attention because of its advantages of fast-speed, high efficiency, pollution-free and non-destructive analysis. One-dimensional Convolution Neural Network (1D-CNN) is a kind of pre-feedback network with a deep structure, including Convolution operation. It has been successfully applied in the analysis of speech and vibration signals. The combination of the One-Dimensional Convolution Neural Network and the Raman spectral data of the main genotypes of mycoplasma pneumoniae M129 and FH were used as the research objects to realize mycoplasma classification pneumoniae strains. The spectral data enhancement method expands the original spectral data set, and the one-dimensional convolution neural network model was trained, and the problem of data hunger of convolutional neural network caused by small samples was solved. In order to obtain the best classification effect of mycoplasma pneumoniae and accelerate the learning process, the model structure was optimized, and the best model parameters were determined. Gaussian noise, Poisson noise and multiplicative noise are often mixed in Raman spectral measurement. Gaussian noise, Poisson noise and multiplicative noise are often mixed in Raman spectral measurement. In order to optimize the anti-noise ability of the model, Gaussian noise, Poisson noise and multiplicative noise were superimposed on the original spectrum respectively, and the 1D-CNN model was trained and compared with the models built by traditional algorithms such as LDA, KNN and SVM. The experimental results show that for the Raman spectra superimposed with Gaussian noise, Poisson noise and multiplicative noise, the classification accuracy of the models based on 1D-CNN method has achieved 98.0%, 97.0% and 97.0%, respectively, which are all much higher than those of the models based on LDA, KNN and SVM algorithms. At the same time, the 1D-CNN model can achieve 92.5% classification accuracy when the noise reacheds the 55 dBW interference factor, aiming at the noise with different intensities of 5, 15, 25, 35, 45 and 55 dBW.Therefore, it is feasible to apply a one-dimensional convolutional neural network combined with Raman spectrum technology to the classification of mycoplasma pneumoniae strain types, which has the advantages of strong anti-noise ability and high classification accuracy. This study provides a new idea for the rapid diagnosis of mycoplasma pneumoniae pneumonia.
|
Received: 2021-04-13
Accepted: 2021-08-09
|
|
Corresponding Authors:
MENG Zong
E-mail: mzysu@ysu.edu.cn
|
|
[1] Waites K B, Xiao L, Liu Y, et al. Clinical Microbiology Reviews, 2017, 30(3): 747.
[2] He J, Liu M H, Ye Z F, et al. Molecular Medicine Reports, 2016, 14(5): 4030.
[3] Parrott G L, Kinjo T, Fujita J. Frontiers in Microbiology, 2016, 7: 513.
[4] Loens K, Leven M. Frontiers in Microbiology, 2016, 7: 448.
[5] Miyashita N, Kawai Y, Kato T, et al. Journal of Infection and Chemotherapy, 2016, 22(5): 327.
[6] Sano G, Itagaki T, Ishiwada N, et al. Journal of Medical Microbiology, 2016, 65(10): 1105.
[7] Khan S, Ullah R, Khan A, et al. Photodiagnosis and Photodynamic Therapy, 2018, 23(9): 89.
[8] Chen C, Yang L, Zhao J, et al. Optik, 2020, 203: 164043.
[9] Liu J, Osadchy M, Ashton L, et al. Analyst, 2017, 142: 4067.
[10] Shao X, Zhang H, Wang Y, et al. Nanomedicine: Nanotechnology, Biology and Medicine, 2020, 29: 102245.
[11] LI Qing-xu, WANG Qiao-hua, GU Wei, et al(李庆旭, 王巧华, 顾 伟, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(12): 3847.
[12] Henderson K C, Benitez A J, Ratliff A E, et al. PLOS ONE, 2015, 10(6): e0131831.
|
[1] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
[2] |
WANG Fang-yuan1, 2, HAN Sen1, 2, YE Song1, 2, YIN Shan1, 2, LI Shu1, 2, WANG Xin-qiang1, 2*. A DFT Method to Study the Structure and Raman Spectra of Lignin
Monomer and Dimer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 76-81. |
[3] |
XING Hai-bo1, ZHENG Bo-wen1, LI Xin-yue1, HUANG Bo-tao2, XIANG Xiao2, HU Xiao-jun1*. Colorimetric and SERS Dual-Channel Sensing Detection of Pyrene in
Water[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 95-102. |
[4] |
WANG Xin-qiang1, 3, CHU Pei-zhu1, 3, XIONG Wei2, 4, YE Song1, 3, GAN Yong-ying1, 3, ZHANG Wen-tao1, 3, LI Shu1, 3, WANG Fang-yuan1, 3*. Study on Monomer Simulation of Cellulose Raman Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 164-168. |
[5] |
LAN Yan1,WANG Wu1,XU Wen2,CHAI Qin-qin1*,LI Yu-rong1,ZHANG Xun2. Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 158-163. |
[6] |
WANG Lan-hua1, 2, CHEN Yi-lin1*, FU Xue-hai1, JIAN Kuo3, YANG Tian-yu1, 2, ZHANG Bo1, 4, HONG Yong1, WANG Wen-feng1. Comparative Study on Maceral Composition and Raman Spectroscopy of Jet From Fushun City, Liaoning Province and Jimsar County, Xinjiang Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 292-300. |
[7] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
[8] |
WANG Zhi-qiang1, CHENG Yan-xin1, ZHANG Rui-ting1, MA Lin1, GAO Peng1, LIN Ke1, 2*. Rapid Detection and Analysis of Chinese Liquor Quality by Raman
Spectroscopy Combined With Fluorescence Background[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3770-3774. |
[9] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1,ZHAO Yan-jie1*,NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
[10] |
LU Wen-jing, FANG Ya-ping, LIN Tai-feng, WANG Hui-qin, ZHENG Da-wei, ZHANG Ping*. Rapid Identification of the Raman Phenotypes of Breast Cancer Cell
Derived Exosomes and the Relationship With Maternal Cells[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3840-3846. |
[11] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
[12] |
GUO He-yuanxi1, LI Li-jun1*, FENG Jun1, 2*, LIN Xin1, LI Rui1. A SERS-Aptsensor for Detection of Chloramphenicol Based on DNA Hybridization Indicator and Silver Nanorod Array Chip[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3445-3451. |
[13] |
ZHU Hua-dong1, 2, 3, ZHANG Si-qi1, 2, 3, TANG Chun-jie1, 2, 3. Research and Application of On-Line Analysis of CO2 and H2S in Natural Gas Feed Gas by Laser Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3551-3558. |
[14] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[15] |
LIU Jia-ru1, SHEN Gui-yun2, HE Jian-bin2, GUO Hong1*. Research on Materials and Technology of Pingyuan Princess Tomb of Liao Dynasty[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3469-3474. |
|
|
|
|