## 3 Experimental Results  
A total of 1200 retinal images with the resolution of 3872 \(\times\) 2592 pixels in 24-bit JPEG format from Bhumibol Adulyadej Hospital were used to evaluate our proposed method in which 70% of images were used for training and the remaining images were used for testing. The images were separated into 7 groups by the ophthalmologists as illustrated in Table 2.  
In this paper, the proposed method was tested using MATLAB R2016b on a laptop with i3 processor and 2 GB RAM. The experiment was conducted as outlined in the following procedure:  
1. All appeared lesions were segmented by improving our previous method in Reference [17]. 2. Eight feature sets namely morphological features, intensity features, color features, first order statistical features, Gray Level Co-occurrence Matrix (GLCM) features, Gray Level Run Length Matrix (GLRLM) features, local binary pattern features and Tamura's texture features were extracted. 3. The highlights comparative studies on Genetic Algorithm (GA) [28], Particle Swarm Optimization (PSO) [29], Hybrid Ant Colony Optimization (HACO) [26] and HSA Optimization were performed to get the best optimal feature set. \begin{table}
\begin{tabular}{c c} \hline \hline
**Pathological Signs of Retinal Images** & **Number of Images** \\ \hline Healthy retinal images & 218 \\ Mild and Moderate NPDR & 318 \\ Severe NPDR & 312 \\ New blood vessels growing & 36 \\ Neovascularization & 115 \\ Fibrous proliferations & 160 \\ Scar & 41 \\ \hline Total & 1200 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of each pathological signs of retinal images. Figure 7: Flowchart of ensemble bagging classifier. 4. The optimal feature set was used as input to classifiers. Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Ensemble Bagging (EB) based classifiers were employed. 5. The performance of each classifiers coupling GA, PSO, HACO, and HSA were respectively evaluated using six performance measures: sensitivity, specificity, accuracy, F-measure, precision and the Area Under Receiver Operating Characteristic curve (AUROC). To make the quantitative evaluation of the classification performance of the proposed method, we compared the classification results with five performance measures: sensitivity, specificity accuracy, F-measure, and precision. The performance measures were obtained based on the combination of different classifiers and feature selections as tabulated in Tables 3-6 respectively. The performance measures were also evaluated using AUROC as presented in Table 7. The experimental results in Tables 3-6 show that HSA coupling with EB achieved the best performance compared to other coupling pairs in this study as well as compared to the related works in the literature as given in Table 8. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifiers** & **Sensitivity** & **Specificity** & **Accuracy** & **Precision** & **F-measure** \\ \hline SVM & 85.45\% & 95.13\% & 92.92\% & 83.93\% & 84.68\% \\ DT & 80.00\% & 96.21\% & 92.50\% & 86.27\% & 83.02\% \\ LR & 58.18\% & 95.13\% & 86.67\% & 78.05\% & 66.67\% \\ LDA & 90.90\% & 68.65\% & 73.75\% & 46.29\% & 61.35\% \\ KNN & 90.90\% & 94.05\% & 93.33\% & 81.97\% & 86.20\% \\ EB & 90.90\% & 96.75\% & 95.42\% & 89.28\% & 90.90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification results based on hybrid ant colony optimization coupling with different classifiers. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifiers** & **Sensitivity** & **Specificity** & **Accuracy** & **Precision** & **F-measure** \\ \hline SVM & 89.09\% & 96.75\% & 95.00\% & 89.09\% & 89.09\% \\ DT & 87.27\% & 94.05\% & 92.50\% & 81.35\% & 84.21\% \\ LR & 72.72\% & 94.59\% & 89.58\% & 80.00\% & 76.19\% \\ LDA & 90.90\% & 68.64\% & 73.75\% & 46.29\% & 61.35\% \\ KNN & 85.45\% & 95.13\% & 92.91\% & 83.92\% & 84.68\% \\ EB & 90.90\% & 98.92\% & 97.08\% & 96.15\% & 93.45\% \\ \hline \hline \end{tabular}
\end{table}