Optics and Precision Engineering, Volume. 32, Issue 5, 714(2024)

DRT Net: dual Res-Transformer pneumonia recognition model oriented to feature enhancement

Tao ZHOU1,2, Caiyue PENG1,2、*, Yuhu DU1,2, Pei DANG1,2, Fengzhen LIU1,2, and Huiling LU3
Author Affiliations
  • 1College of Computer Science and Engineering, North Minzu University, Yinchuan75002, China
  • 2Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan75001, China
  • 3School of Medical Information & Engineering, Ningxia Medical University, Yinchuan750004, China
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    Figures & Tables(14)
    Overall framework of DRT Net
    Group attention dual residual module
    Channel shuffle operation
    Squeeze excitation module and spatial attention module
    Global local feature extraction module
    Cross-layer dual attention feature fusion module
    Dataset display
    Radar chart of ablation experiment results
    Confusion matrix of each model in ablation experiments
    Radar chart of pneumonia classification results for different models
    Confusion matrix of classification results for each model
    • Table 1. Result comparison of ablation experiments

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      Table 1. Result comparison of ablation experiments

      NameModelGADRMGLFEMCDAFFMAPRF1AUC
      Network1DPN920.969 20.912 80.902 60.900 30.988 4
      Network2DPN92+CDAFFM0.972 50.927 00.915 50.919 00.990 1
      Network3DPN92+GLFEM0.974 10.928 80.920 50.921 10.991 2
      Network4DPN92+GADRM0.974 70.936 30.942 70.938 60.992 8
      Network5DPN92+GLFEM+CDAFFM0.974 60.930 90.913 80.920 00.992 7
      Network6DPN92+GADRM+GLFEM0.975 50.923 50.917 30.915 30.992 0
      Network7DPN92+GADRM+CDAFFM0.977 90.939 10.937 60.937 40.993 0
      Network8DRT Net0.984 10.944 20.942 00.942 60.996 5
    • Table 2. Classification results of pneumonia X-ray images for each model

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      Table 2. Classification results of pneumonia X-ray images for each model

      ModelAPRF1AUC
      ResNet5050.959 60.914 20.907 40.905 10.986 9
      ResNet10150.965 10.927 00.919 40.920 30.991 0
      Res2Net50180.966 40.907 50.918 40.908 80.990 4
      Densenet121190.969 10.929 00.912 50.916 70.991 2
      ResNeXt101200.963 30.921 20.918 70.917 40.990 8
      MobileNetV2210.964 70.913 90.894 40.897 10.989 4
      DPN92220.969 20.912 80.902 60.900 30.988 4
      Swin Transformer230.940 30.900 70.868 40.876 20.979 1
    • Table 2. Classification results of pneumonia X-ray images for each model

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      Table 2. Classification results of pneumonia X-ray images for each model

      ModelAPRF1AUC
      Mobile vit240.974 10.930 70.918 40.922 30.988 6
      ReNeSt50250.961 80.910 50.902 80.904 10.982 7
      ConvNeXt_XL260.966 40.893 00.854 60.854 30.989 4
      DRT Net0.984 10.944 20.942 00.942 60.996 5
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    Tao ZHOU, Caiyue PENG, Yuhu DU, Pei DANG, Fengzhen LIU, Huiling LU. DRT Net: dual Res-Transformer pneumonia recognition model oriented to feature enhancement[J]. Optics and Precision Engineering, 2024, 32(5): 714

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    Paper Information

    Category:

    Received: May. 11, 2023

    Accepted: --

    Published Online: Apr. 2, 2024

    The Author Email: Caiyue PENG (peng_caiyue@163.com)

    DOI:10.37188/OPE.20243205.0714

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