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Laser & Optoelectronics Progress, Volume. 61, Issue 18, 1817001(2024)

Classification of Microscopic Hyperspectral Images of Cancerous Tissue Based on Deep Learning

Yong Zhang1,2, Danfei Huang1,2、*, Lechao Zhang1,2, Lili Zhang1,2, Yao Zhou1,2, and Hongyu Tang1,2
Author Affiliations
  • 1School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, Jilin, China
  • 2Zhongshan Institute, Changchun University of Science and Technology, Zhongshan 528400, Guangdong, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (7)
    Equations (5)
    References (26)
    Cited By (1)
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    Figures & Tables(7)
    Microhyperspectral imaging system

    Fig. 1. Microhyperspectral imaging system

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    CBAM-RFNet network structure

    Fig. 2. CBAM-RFNet network structure

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    Two residual layer structures of ResNet model and strcture of RF-block. (a) "Non-bottleneck" structure of ResNet network; (b) "bottleneck" structure of ResNet network; (c) RF-block structure

    Fig. 3. Two residual layer structures of ResNet model and strcture of RF-block. (a) "Non-bottleneck" structure of ResNet network; (b) "bottleneck" structure of ResNet network; (c) RF-block structure

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    CBAM module structure. (a) Schematic diagram of CBAM module; (b) CAM structure of CBAM; (c) SAM structure of CBAM

    Fig. 4. CBAM module structure. (a) Schematic diagram of CBAM module; (b) CAM structure of CBAM; (c) SAM structure of CBAM

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    Thyroid tissue image. (a) Gray image of cancerous tissue; (b) false color image of cancerous tissue; (c) gray map of normal tissue; (d) false color images of normal tissue

    Fig. 5. Thyroid tissue image. (a) Gray image of cancerous tissue; (b) false color image of cancerous tissue; (c) gray map of normal tissue; (d) false color images of normal tissue

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    CBAM-RFNet (N=1) model identification results. (a) (b) Color image of tissue; (c) (d) false color diagram of tissue

    Fig. 6. CBAM-RFNet (N=1) model identification results. (a) (b) Color image of tissue; (c) (d) false color diagram of tissue

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    • Table 1. Classification results of different algorithms on data set

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      Table 1. Classification results of different algorithms on data set

      AlgorithmOA /%F1 /%Kappa coefficientTotal parameters
      CBAM-RFNet（N=1）98.2398.660.909886884
      CBAM-RFNet（N=2）97.3297.970.8891393764
      CBAM-RFNet（N=3）97.5898.170.8915667940
      CBAM-RFNet（N=4）96.2497.170.8565706340
      RFNet（N=1）96.1397.150.812624642
      RFNet（N=2）96.2497.160.8001131522
      RFNet（N=3）97.7898.320.8944619266
      RFNet（N=4）97.2197.900.8854657666
      ResNet3496.3297.190.78221702786
      ResNet5096.9297.680.85023929218
      ResNet10196.2197.130.77042921346
      U-Net92.0894.080.560275943688
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    Yong Zhang, Danfei Huang, Lechao Zhang, Lili Zhang, Yao Zhou, Hongyu Tang. Classification of Microscopic Hyperspectral Images of Cancerous Tissue Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2024, 61(18): 1817001

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    Paper Information

    Category: Medical Optics and Biotechnology

    Received: Jan. 27, 2024

    Accepted: Mar. 7, 2024

    Published Online: Sep. 9, 2024

    The Author Email: Danfei Huang (danfei_huang@163.com)

    DOI:10.3788/LOP240755

    CSTR:32186.14.LOP240755

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology