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Table 2 Results for each model doing deep tuning and 10-fold cross validation

From: CNNs for automatic glaucoma assessment using fundus images: an extensive validation

Model name AUC 95% confidence interval Accuracy Specificity Sensitivity Fscore P-value
VGG16 [15] 0.9632 (0.0149) 95.81–96.87% 0.8948 (0.0253) 0.8816 (0.0612) 0.9057 (0.0331) 0.9005 (0.0231) 0.3240
VGG19 [15] 0.9686 (0.0158) 96.45–97.39% 0.9069 (0.0318) 0.8846 (0.0362) 0.9240 (0.0434) 0.9125 (0.0312) 0.3607
InceptionV3 [30] 0.9653 (0.0135) 96.12–97.32% 0.9000 (0.0201) 0.8752 (0.0358) 0.9216 (0.0311) 0.9056 (0.0236) 0.3126
ResNet50 [32] 0.9614 (0.0171) 95.62–96.77% 0.9029 (0.0249) 0.8943 (0.0350) 0.9105 (0.0282) 0.9076 (0.0251) 0.3885
Xception [33] 0.9605 (0.0170) 95.92–97.07% 0.8977 (0.0264) 0.8580 (0.0398) 0.9346 (0.0247) 0.9051 (0.0274) 0.2729