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Table 1 Comparison between TIA-Net and models in three benchmark sets

From: Automatic glaucoma detection based on transfer induced attention network

Database Methods Acc (%) Se (%) Sp (%) AUC
Ours HOS-LR 69.9 91.1 55.6 0.719
Wavelet-LR 68.9 69.5 58.9 0.715
Gabor-LR 70.5 86.7 62.2 0.776
HWG 72.1 93.2 61.9 0.802
CNN 80.2 91.4 77.0 0.869
VGG 80.7 87.7 79.1 0.871
GoogLeNet 79.8 80.7 73.8 0.870
ResNet 81.2 83.6 73.9 0.872
Chen [11] 80.9 89.1 77.8 0.875
Shibata [29] 81.7 87.5 80.2 0.879
NMD+CNN 84.1 84.7 83.4 0.911
SOD+CNN 83.7 84.2 80.6 0.903
NMD+Attention 84.5 84.4 84.9 0.911
TIA-Net (SOD+Attention) 85.7 84.9 86.9 0.929
ORIGA HOS-LR 63.5 90.3 32.2 0.632
Wavelet-LR 65.9 59.1 66.8 0.648
Gabor-LR 67.2 49.0 77.2 0.682
HWG 68.8 71.7 55.0 0.693
CNN 70.4 70.7 74.8 0.791
VGG 70.1 69.8 71.0 0.800
GoogLeNet 71.8 69.8 73.5 0.805
ResNet 71.5 71.3 71.7 0.803
Chen [11] 70.8 69.2 71.0 0.794
Shibata [29] 73.3 73.2 76.7 0.809
NMD+CNN 74.5 68.7 80.7 0.815
SOD+CNN 73.9 80.9 72.2 0.813
NMD+Attention 74.9 71.2 77.7 0.817
TIA-Net (SOD+Attention) 76.6 75.3 77.2 0.835
  1. Acc, Se, and Sp represent accuracy, sensitivity, and specificity, respectively