## 5 Discussion  
Rather than classify the severity level of a certain disease, we have opted in this work to focus on distinguishing between three classes, namely DR, glaucoma, and normal. The reason why our system performed remarkably well is multi-faceted. First, the choice of the features (of the three groups) plays an important role. For example, we used entropy and ASM as features because the former is a measure of disorder, just as the latter is a measure of sameness and consistency in the image. The higher the entropy, the higher the disorder (pathological pattern-like DR) in the image, and vice versa. So, entropy is a good biomarker for the degree of DR in an image. On the other hand, ASM is a good biomarker for normal and DR images. In addition, energy is a good biomarker for the local uniformity or homogeneity that can differentiate between DR and glaucoma. Furthermore, contrast is a good biomarker for DR, particularly in the green channel. So the obtained GLCM in general provides good biomarkers for discerning between DR and normal images. Meanwhile, the HOG features are a good biomarker for glaucoma through cropping the OD of the pixels. During the feature extraction and selection procedure, we observed \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Class** & \(\mathbf{Pr}\) & \(\mathbf{Se}\) & \(\mathbf{Sp}\) & \(\mathbf{F1}\) & \(\mathbf{Acc}\) \\ \hline \multirow{4}{*}{[17]} & Melanoma & 0.8193 & 0.9701 & 0.9511 & 0.8883 & 0.9479 \\  & Glaucoma & 0.8388 & 0.9365 & 0.9619 & 0.8850 & 0.9510 \\  & AMD & 0.8090 & 0.8317 & 0.9516 & 0.8202 & 0.9196 \\  & DR & 0.8829 & 0.7211 & 0.9711 & 0.7938 & 0.9047 \\  & Normal & 0.7788 & 0.7136 & 0.9296 & 0.7448 & 0.8894 \\ \hline \multirow{4}{*}{The proposed model via DT classifier} & DR & 1.000 & 0.9090 & 1.000 & 0.9523 & 0.9667 \\  & Glaucoma & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \cline{1-1}  & Normal & 0.9000 & 1.000 & 0.9524 & 0.9473 & 0.9667 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison between the results of the proposed model and the state-of-the-art method as an automatic deep feature extraction. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & & \(\mathbf{Pr}\) & \(\mathbf{Se}\) & \(\mathbf{Sp}\) & \(\mathbf{F1}\) & \(\mathbf{Acc}\) & \(\mathbf{AUC}\) \\ \hline DR & \(Best(\max)\) & 0.8000 & 0.7700 & 0.8947 & 0.7619 & 0.8334 & 0.8110 \\  & \(Worst(\min)\) & 0.4000 & 0.5000 & 0.7142 & 0.4444 & 0.6551 & 0.6071 \\ \hline Glaucoma & \(Best(\max)\) & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1 \\  & \(Worst(\min)\) & 0.8000 & 0.5714 & 0.8467 & 0.6667 & 0.7037 & 0.7087 \\ \hline Normal & \(Best(\max)\) & 0.8887 & 0.8889 & 0.8421 & 0.8752 & 0.8752 & 0.8777 \\  & \(Worst(\min)\) & 0.7000 & 0.7000 & 0.8000 & 0.7000 & 0.7857 & 0.7666 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The best and the worst classification results for the HRF dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & & \(\mathbf{Pr}\) & \(\mathbf{Se}\) & \(\mathbf{Sp}\) & \(\mathbf{F1}\) & \(\mathbf{Acc}\) & \(\mathbf{AUC}\) \\ \hline DR & \(Best(\max)\) & 1.000 & 1.000 & 1.000 & 0.9523 & 0.9667 & 0.9545 \\  & \(Worst(\min)\) & 0.7000 & 0.8750 & 0.8636 & 0.7778 & 0.8667 & 0.8693 \\ \hline Glaucoma & \(Best(\max)\) & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\  & \(Worst(\min)\) & 0.9000 & 0.8082 & 0.9445 & 0.8571 & 0.8965 & 0.8965 \\ \hline Normal & \(Best(\max)\) & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.9761 \\  & \(Worst(\min)\) & 0.9000 & 0.8181 & 0.9444 & 0.8571 & 0.8965 & 0.8813 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The best and the worst classification results for Dataset_2. that the combination of the three feature sets is wholly leveraged to discern within every pathological pattern in the retinal image. The classification results are provided comprehensively in Section 4, not only as overall metrics for all three classes (DR, glaucoma, and normal) but also for each class individually.