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Table 2 Performance of the algorithm and the compared methods

From: Automatic differentiation of Glaucoma visual field from non-glaucoma visual filed using deep convolutional neural network

  Methods Accuracy Specificity Sensitivity
Ophthalmologists resident #1 0.640 0.767 0.513
resident #2 0.593 0.680 0.507
resident #3 0.587 0.630 0.540
attending #1 0.533 0.213 0.853
attending #2 0.570 0.670 0.473
attending #3 0.653 0.547 0.760
glaucoma expert #1 0.663 0.700 0.647
glaucoma expert #2 0.607 0.527 0.687
glaucoma expert #3 0.607 0.913 0.300
Rule based methods AGIS 0.459 0.560 0.343
GSS2 0.523 0.500 0.550
Traditional machine learning methods SVM 0.670 0.618 0.733
RF 0.644 0.453 0.863
k-NN 0.591 0.347 0.870
  CNN 0.876 0.826 0.932