## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Number of Images**} & \multirow{2}{*}{**Ground Truth Information**} & \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Avail**} \\ \cline{2-2} \cline{5-6}  & **Norm** & & & & & Pixel-wise annotations of & \\  & & & & OD and OC & & \\ REFUGE & 1080 & 120 & 1200 & Localization mask of Fovea & [PERSON] et al. [5] & Online \\  & & & & Classification labels of & & \\  & & & & normal and glaucomatous & & \\ \hline ACRIMA & 309 & 396 & 705 & Classification labels of & & \\  & & & & normal and glaucomatous & [PERSON] et al. [6] & Online \\ \hline ORIGA & 482 & 168 & 650 & Segmentation masks of OD & & \\  & & & & and OC & & \\  & & & & Classification labels of & & \\  & & & & normal and glaucomatous & & \\ \hline RIMONE & 92 & 39 & 131 & Manual segmentation & & \\  & & & & masks of OD & \\  & & & & Classification labels of & & \\  & & & & normal and glaucomatous & & \\ \hline DRISHTI-GS1 & 31 & 70 & 101 & Manual segmentation & & \\  & & & & & mass of optic nerve head & \\  & & & & & for 50 training images & \\  & & & & & Classification labels of & \\  & & & & & normal and glaucomatous & \\ \hline HRF &
\begin{tabular}{c} 15 \\ • DR 15 \\ • \end{tabular} & 15 & 45 & Segmentation masks of & & \\  & & & & field of view (FOV), blood & \\  & & & & vessels and OD & \\  & & & & Classification Labels of & \\  & & & & Normal, DR and & \\  & & & & & glaucomatous & \\ \hline SiMES & 482 & 168 & 650 & Classification labels of & & \\  & & & & normal and glaucomatous & \\ \hline SCES & 1630 & 46 & 1676 & Classification labels of & & \\  & & & normal and glaucomatous & & \\ \hline SiNDI & 5670 & 113 & 5783 & Classification labels of & & \\  & & & normal and glaucomatous & & \\ \hline \hline \end{tabular} Note: DR—diabetic retinopathy; Norm—normal; Glau—glaucoma; Avail—available.  
\end{table} 
Table 1: Datasets for glaucoma detection. datasets, SiMES dataset and SCES dataset, separately and achieved AUC of 83% and 88%, respectively. [PERSON] et al. [15] used Hough transform to detect OD region, extracted the texture of projection and bag of words features from the detected OD, and finally trained SVM classifier to discriminate between healthy and glaucoma OD. They obtained an accuracy of 76.8% and AUC of 78.0% on DRISTI-GS1 dataset. [PERSON] et al. [16] used superpixel segmentation to retrieve OD and OC regions, and measured CDR, inferior-superior-nasal-temporal (ISNT), distance between central OD and optic nerve head, the area of blood vessels inside optic nerve head and intensity value between central OD and optic nerve head. Neural networks were trained using the measurements as the attributes to identify the abnormality and obtained 100% accuracy on 26 images. However, their results were hampered by validating on a small number of images. [PERSON] et al. [17] preprocessed images to remove noise and enhance the contrast, then segmented the OD and OC regions using simple linear iterative clustering superpixels. Finally, CDR was computed to determine the presence of glaucoma. They reported the value of CDR between 0.4 and 0.6 for the class of non-glaucomatous images and greater than 0.6 is for glaucomatous image. [PERSON] et al. [18] directly extracted the features using a 2-D discrete wavelet transform from the entire image and fed them to train a neural network. They experimented on 45 images from HRF dataset and obtained 95.8% accuracy. [PERSON] et al. [19] applied wavelet transform method to decompose the retinal image, then extracted 12 core entropy features from four different color channels--red, green, blue and grayscale. Finally, the obtained features were input to least squares support vector machine. The accuracy of 81.3% was reported on RIM-ONE dataset. These studies required domain knowledge from experts to design hand-crafted features.