• Acta Optica Sinica
  • Vol. 40, Issue 3, 0310002 (2020)
Sheng Huang, Feifei Li**, and Qiu Chen*
Author Affiliations
  • School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  • show less
    DOI: 10.3788/AOS202040.0310002 Cite this Article Set citation alerts
    Sheng Huang, Feifei Li, Qiu Chen. Computed Tomography Image Classification Algorithm Based on Improved Deep Residual Network[J]. Acta Optica Sinica, 2020, 40(3): 0310002 Copy Citation Text show less
    Residual block
    Fig. 1. Residual block
    Architecture of SK-ResNet
    Fig. 2. Architecture of SK-ResNet
    Structure of discriminator
    Fig. 3. Structure of discriminator
    Process of unsupervised pre-training
    Fig. 4. Process of unsupervised pre-training
    Example of labeled areas of lung (areas 1, 2 denote pathology area)
    Fig. 5. Example of labeled areas of lung (areas 1, 2 denote pathology area)
    Image patch examples. (a) Source domain; (b) target domain
    Fig. 6. Image patch examples. (a) Source domain; (b) target domain
    Trend graph of classification results for training sets with different scales
    Fig. 7. Trend graph of classification results for training sets with different scales
    Misclassified examples
    Fig. 8. Misclassified examples
    SchemeSize of training setSize of test setSplit manner
    A413210315-fold
    B25602479Case select
    C11289750Random select
    Table 1. Different dataset separation schemes
    NetworkStructure ofResNet blockfavg
    ABC
    SK-ResNet 10[1,1,1,1]0.95860.94530.9651
    SK-ResNet 14[2,2,1,1]0.96090.94760.9640
    SK-ResNet 18[2,2,2,2]0.95330.93540.9553
    SK-ResNet 34[3,4,6,3]0.94970.93290.9549
    Table 2. Comparison of classification results of SK-ResNet with different depths
    MethodABC
    Without transfer0.96090.94760.9640
    DIM0.97990.96540.9707
    DIM+PM0.98180.96770.9756
    Table 3. Comparison of results on SK-ResNet before and after using transfer learning
    Ground truthPredicted result
    EMFBGGNMMN
    EM0.91000.080.01
    FB00.960.0400
    GG00.020.9800
    NM000.030.940.03
    MN0000.060.94
    Table 4. Classification confusion matrix of SK-ResNet
    Ground truthPredicted result
    EMFBGGNMMN
    EM0.97000.030
    FB00.960.0400
    GG00.020.9800
    NM000.030.960.01
    MN0000.030.97
    Table 5. Classification confusion matrix of SK-ResNet using transfer learning
    MethodABC
    Ref.[14]0.77250.76440.7987
    Ref. [16]0.94650.92150.9392
    Ref. [23]0.94830.92690.9554
    AlexNet[11]0.89620.88210.9226
    Pre-trained AlexNet[11]0.94710.93370.9609
    Pre-trained VGG-16[24]0.96030.94350.9664
    ResNet-18[17]0.93710.93060.9432
    Pre-trained ResNet-18 [17]0.95810.94430.9651
    Pre-trained DenseNet-121[25]0.97410.95790.9715
    SK-ResNet0.96090.94760.9640
    SK-ResNet (transfer)0.98180.96770.9756
    Table 6. Comparison of classification performances of SK-ResNet and other methods
    Sheng Huang, Feifei Li, Qiu Chen. Computed Tomography Image Classification Algorithm Based on Improved Deep Residual Network[J]. Acta Optica Sinica, 2020, 40(3): 0310002
    Download Citation