## 4 Results
Furthermore, we obtained the \(\textit{Overall}\;\;Se,\textit{Overall}\;\;Sp\), and \(\textit{Overall}\;\;Acc\) parameters, which are calculated from the aggregation of all three classes for each dataset as follows. \[\textit{Overall}\;\;Se=\frac{\sum_{i=1}^{3}\texttt{TP}_{i}}{\sum_{i=1}^{3} \texttt{TP}_{i}+\texttt{FN}_{i}} \tag{28}\] \[\textit{Overall}\;\;Sp=\frac{\sum_{i=1}^{3}\texttt{TN}_{i}}{\sum _{i=1}^{3}\texttt{TN}_{i}+\texttt{FP}_{i}}\] (29) \[\textit{Overall}\;\;Acc=\frac{\sum_{i=1}^{3}\texttt{(TP}_{i}+ \texttt{TN}_{i})}{\sum_{i=1}^{3}\texttt{(TP}_{i}+\texttt{FP}_{i}+\texttt{TN} _{i}+\texttt{FN}_{i})} \tag{30}\]  
where \(i=1,2,3\) is the class label with \(1=\text{DR}\), \(2=\text{glaucoma}\) and \(3=\text{normal}\). Unlike most previous studies, we evaluate the classifiers of the proposed system in two ways. First, the performance metrics (22)-(27) are used to measure the classification performance for every classifier for each class (DR, glaucoma, and normal). Second, the performance metrics (28)-(30) are used to measure the overall classification performance for all classes. Tables 5 and 6 show the results regarding precision, sensitivity, specificity, \(F1\) score, and accuracy metrics for the two datasets. In all tables, the proposed system results are shown in detail for each class and for each dataset. From the tables, it is obvious that the  
proposed system functions remarkably well. Tables 5 and 6 represent the classification performance of the four classifiers when tested on the HRF features and Dataset_2 features, respectively. Each row represents results for only one class. Table 7 represents the classification performance for the two datasets as overall sensitivity (_Overall Se_), overall specificity (_Overall Sp_), and overall accuracy (_Overall Acc_). Clearly, the proposed system performs extremely well. Tables 8 and 9 show the best and worst results for each class in the HRF dataset and Dataset_2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classifier** & **Class** & \(\mathbf{Pr}\) & \(\mathbf{Se}\) & \(\mathbf{Sp}\) & \(\mathbf{F1}\) & \(\mathbf{Acc}\) & \(\mathbf{AUC}\) \\ \hline \multirow{3}{*}{DT} & DR & 1 & 0.9090 & 1 & 0.9523 & 0.9667 & 0.9545 \\  & Glaucoma & 1 & 1 & 1 & 1 & 1 \\  & Normal & 0.9000 & 1 & 0.9524 & 0.9473 & 0.9667 & 0.9761 \\ \hline \multirow{3}{*}{kNN} & DR & 0.7000 & 0.8750 & 0.8636 & 0.7778 & 0.8667 & 0.8693 \\  & Glaucoma & 1 & 0.9000 & 1 & 0.9524 & 0.9629 & 0.9545 \\  & Normal & 0.9001 & 0.8181 & 0.9445 & 0.8571 & 0.8965 & 0.8813 \\ \hline \multirow{3}{*}{NB} & DR & 0.7000 & 1 & 0.8695 & 0.8235 & 0.9000 & 0.9347 \\  & Glaucoma & 1 & 0.9090 & 1 & 0.9523 & 0.9642 & 0.9545 \\  & Normal & 1 & 0.8333 & 1 & 0.9090 & 0.9310 & 0.9166 \\ \hline \multirow{3}{*}{LDA} & DR & 0.8000 & 1 & 0.9000 & 0.8888 & 0.9285 & 0.9500 \\  & Glaucoma & 0.9000 & 0.8182 & 0.9445 & 0.8571 & 0.8965 & 0.8813 \\ \cline{1-1}  & Normal & 0.9000 & 0.8182 & 0.9444 & 0.8571 & 0.8965 & 0.8813 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification performance per classifier for every class in Dataset_2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Classifier** & _Overall Se_ & _Overall Sp_ & _Overall Acc_ \\ \hline \multirow{3}{*}{HRF dataset} & DT & 0.7400 & 0.0740 & 0.7400 \\  & \(k\)NN & 0.6600 & 0.6600 & 0.6600 \\  & NB & 0.6400 & 0.6400 & 0.6400 \\  & LDA & 0.8300 & 0.8300 & 0.8300 \\ \hline \multirow{3}{*}{Dataset\_2} & DT & 0.9667 & 0.9667 & 0.9667 \\  & \(k\)NN & 0.8670 & 0.8670 & 0.8670 \\  & NB & 0.9000 & 0.9000 & 0.9000 \\  & LDA & 0.9360 & 0.9360 & 0.9360 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Overall classification performance for the two datasets.