## 3 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Pretrained Deep**} & \multicolumn{4}{c}{**REFUGE**} & \multicolumn{4}{c}{**RIM-ONE**} & \multicolumn{4}{c}{**ACRIMA**} & \multicolumn{2}{c}{**ORGIA**} & \multicolumn{2}{c}{**DRISTI-GS1**} \\ \cline{2-10}
**CNNs** & \begin{tabular}{c} **ACC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **ACC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} \\ \hline AlexNet & 90.00 & 81.69 & 74.87 & 69.21 & 96.23 & 99.85 & 68.42 & 59.62 & 70.00 & 81.48 \\ \hline GoogleNet & 92.50 & 88.69 & 94.74 & 91.03 & 91.51 & 96.49 & 71.79 & 77.03 & 70.00 & 79.89 \\ \hline InceptionV3 & 90.25 & 87.26 & 71.05 & 76.92 & 93.87 & 99.36 & 75.38 & 76.86 & 70.00 & 65.08 \\ \hline XceptionNet & 89.00 & 85.29 & 81.58 & 88.14 & 98.11 & 99.80 & 77.44 & 81.68 & 66.67 & 66.67 \\ \hline ResNet-50 & 93.50 & 92.97 & 92.11 & 98.08 & 95.75 & 99.56 & 75.90 & 80.19 & 73.33 & 78.31 \\ \hline SqueezeNet & 91.00 & 89.58 & **97.37** & **100** & 95.75 & 98.84 & 78.46 & 79.17 & 56.67 & 76.19 \\ \hline ShuffleNet & 93.25 & 94.09 & 92.11 & 97.44 & 96.23 & 99.75 & 72.31 & 80.04 & **86.67** & **78.84** \\ \hline MobileNet & **94.50** & **93.04** & 92.11 & 99.36 & 98.58 & 99.96 & **80.51** & **81.54** & 76.67 & 73.02 \\ \hline DenseNet & **93.00** & **94.64** & 94.74 & 99.04 & **99.53** & **99.98** & 77.44 & 73.92 & 73.33 & 78.31 \\ \hline InceptionResNet & 92.25 & 92.08 & 68.42 & 61.86 & 96.23 & 98.84 & **80.00** & **81.31** & 70.00 & 68.78 \\ \hline NasNet-Large & 92.75 & 90.44 & 86.84 & 95.19 & 96.23 & 99.85 & 73.85 & 77.72 & 70.00 & 69.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: P1: Transfer learning using eleven pretrained networks for glaucoma classification. Figure 10: ROC curves of ensemble methods using five different datasets (**a**) REFUGE, (**b**) RIM-ONE, (**c**) ACRIMA, (**d**) ORIGA and (**e**) DRISTI-GS1. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Performance Measures**} \\ \cline{2-11}
**Ensemble** & \multicolumn{2}{c}{**REFUGE**} & \multicolumn{2}{c}{**RIM-ONE**} & \multicolumn{2}{c}{**ACRIMA**} & \multicolumn{2}{c}{**ORGIA**} & \multicolumn{2}{c}{**DRISTI-GS1**} \\ \cline{2-11}
**Learners** & **ACC** & **AUC** & **ACC** & **AUC** & **(\%)** & **ACC** & **ACC** & **AUC** & **ACC** & **AUC** & **AUC** & **ACC** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Ensemble of & & & & & & & & & & & & \\ P1 (E(P1)) & **95.59** & **95.10** & **97.37** & **100** & **99.53** & **99.98** & **83.59** & **88.86** & 83.33 & 85.19 \\ \hline Ensemble of & & & & & & & & & & & \\ P2 (E(P2)) & **95.75** & **94.32** & 92.11 & 99.04 & 96.23 & 99.01 & 80.00 & 85.26 & **90.00** & **92.06** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: P3: Ensemble of methods in P1 and P2 for glaucoma classification.