## 3 Experimental Results
To evaluate the OD segmentation performance, we counted pixels matching the ground-truth maps. Figure 9 displays the overlap between the white OD mask (ground-truth) and our OD detection (pink mask). Based on the number of pixels overlap between the mask from OD detection and the ground truth mask, three evaluation metrics were computed: accuracy, dice coefficient, and Intersect over Union (IoU). \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{5}\]\[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{6}\]  
\[\text{Dice coefficient}\;=\frac{2\text{TP}}{2\text{TP}+\text{FP}+\text{FN}} \tag{7}\]  
where  
True Positive (TP) is the number of correctly prediction of OD pixels;  
True Negative (TN) is the number of correctly detection of non-OD pixels;  
False Positive (FP) is the number of wrongly detected of non-OD pixels as OD pixels;  
False Negative (FN) is the number of wrongly identified of OD pixels as non-OD pixels. Table 4 shows OD segmentation results using five different deep semantic algorithms. Combinations of DeepLabv3+ with ResNet18, ResNet50, XceptionNet, InceptionResNet and MobileNet models achieved comparable accuracy--between 99.64% and 99.72% with the average computational time 3.7 s per image. For the dice coefficient and IoU, the combination of DeepLabv3+ and MobileNet model achieved the best performance with dice coefficient of 91.73% and IoU of 84.89%. Therefore, the best-performing pair (a combination of DeepLabv3+ and MobileNet) is employed as the OD segmentation algorithm. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{\
\begin{tabular}{c} **No. of** \\ **Images** \\ \end{tabular} } & \multicolumn{4}{c}{**Image Acquisition**} \\ \cline{3-6}  & & **Camera** & **FOV** & **Resolution** & **Ethnicity** & **Focus** \\ \hline \multirow{4}{*}{REFUGE} & \multirow{4}{*}{1200} & Train: Zeiss & \multirow{2}{*}{–} & \multirow{2}{*}{JPEG \(2124\times 2056\)} & \multirow{4}{*}{Chinese} & Center \\  & & Visucam 500 & & & & \\ \cline{3-6}  & &
\begin{tabular}{c} Validation/Testing: \\ Canon CR-2 \\ \end{tabular} & – & JPEG \(1634\times 1634\) & & & OD visible \\ \hline ACRIMA & 705 & Topcon TRC & \(35^{\circ}\) & JPEG \(2048\times 1536\) & Spanish & OD \\ \hline ORIGA & 650 & – & – & JPEG \(3072\times 2048\) & Malay & OD \\ \hline RIM-ONE & 131 & Kowa WX 3D & \(34^{\circ}\) & JPEG \(2144\times 1424\) & Spanish & OD \\ \hline DRISHTI-GS1 & 101 & NM/FA & \(30^{\circ}\) & PNG \(2896\times 1944\) & Indian & OD \\ \hline Total & 2787 & & – & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The datasets for glaucoma detection task. Figure 9: Pixel overlaps between our OD detection (pink masks) and the white ground truth mask. Once OD segmented, it is fed as input to the classification stage. Three proposed methods based on deep learning are applied to identify between non-glaucomatous and glaucomatous OD. We tested each method on all datasets individually to investigate their performance on different datasets. The performance of each method is evaluated and validated using two performance measures: accuracy and area under ROC curve. Table 5 demonstrates the experimental results obtained from the first proposal P1 using eleven different deep CNNs as the transfer learning models. The results showed that DenseNet model achieved the best performance for ACRIMA dataset with an accuracy of 99.53% and AUC of 99.98%, followed by MobileNet and XceptionNet and achieved the similar best performance with MobileNet model for REFUGE dataset with the accuracy of 93.00% and AUC of 94.64% while MobileNet model achieved the accuracy of 94.50% and AUC of 93.04%. MobileNet model achieved the best performance for ORIGA dataset with an accuracy of 80.51% and AUC of 81.54%, followed by InceptionResNet model which obtained the accuracy of 80.00% and AUC of 81.31%.