## 4 Results  
We have tested the proposed system by experimenting extensively on two datasets, originating from three publicly available datasets. Our experiments were run on a platform including Intel(R) Core(TM) i7-9750H CPU @ 2.60 GHz, 2592 MHz, 6 Core(s), 12 logical processor(s), 16 GB memory using MATLAB 9.2, R2017b. The experimental results, presented below, show that the combination of the three sets of features and feature selection process provides a discriminative vector of 105 features, which was later fed into each of the four mentioned classifiers for evaluation. The final results obtained from our experiments are excellent. The first activity we performed was to use the GA to select the most relevant of the 429 originally calculated features. This activity ended up selecting the 105 features shown in Table 3, for which we use the following coding for the feature names:  
* FOSxA is a feature of the FOS group (Section 3.2.1), with \(x=1,2,3,4\) being the feature number within the group, and A \(\in\{\text{R},\text{ G},\text{ B}\}\) being the color channel used. For example, FOS2G is feature no. 2 in the FOS feature family (i.e., variance) calculated for the green channel of the image. * HOSxAy is a feature of the HOS group (Section 3.2.2), with \(x=1,2,\dots,14\), being the feature number within the group, A \(\in\{\text{R},\text{ G},\text{ B}\}\) being the color channel used, and \(y\in\{\text{a},\text{ b},\text{ }\dots,\text{ h}\}\) being the displacement used. For example, HOS9 Rd is feature no. 9 in the HOS feature family (i.e., entropy) calculated for the red channel of the image with the displacement number d in Figure 4.
* HOGxyz is a feature of the HOG group (Section 3.2.3), with \(xy\) referring to entry \((x,y)\) in the \(3\times 3\) matrix and \(z\) being the bin number in the corresponding histogram. For example, HOG125 is the feature referred to by the entry in the 1 st row and 2 nd column of the \(3\times 3\) matrix and the 5 th bin number in the histogram. The four classifiers considered in the present work, DT, kNN, NB, and LDA, were tested separately to classify the HRF dataset and Dataset_2. For each classification experiment, the following \(3\times 3\) confusion matrix was constructed:  
\[\textbf{Actual}\quad \begin{bmatrix}\textbf{Predicted}\\ \textbf{Class}&\textbf{1}&\textbf{2}&\textbf{3}\\ \textbf{1}&A_{11}&A_{12}&A_{13}\\ \textbf{2}&A_{21}&A_{22}&A_{23}\\ \textbf{3}&A_{31}&A_{32}&A_{33}\end{bmatrix} \]  
where \(1=\text{DR}\), \(2=\text{glaucoma}\) and \(3=\text{normal}\), and \(A_{ij}\) is the count of cases of class \(i\) that are classified as class \(j\). This matrix is used to obtain five metrics: precision (\(Pr\)), sensitivity (\(Se\)), specificity (\(Sp\)), \(F1\) score (\(F1\)), accuracy (\(Acc\)), and \(AUC\), as follows. \[Pr=\frac{\texttt{TP}}{\texttt{TP}+\texttt{FP}}, \tag{22}\] \[Se=\frac{\texttt{TP}}{\texttt{TP}+\texttt{FN}},\] (23) \[Sp=\frac{\texttt{TN}}{\texttt{TN}+\texttt{FP}},\] (24) \[F1=\frac{\texttt{2 TP}}{\texttt{2 TP}+\texttt{FP}+\texttt{FN}},\] (25) \[Acc=\frac{\texttt{TP}+\texttt{TN}}{\texttt{TP}+\texttt{FP}+ \texttt{TN}+\texttt{FN}}.\] (26) \[AUC=0.5(\frac{\texttt{TP}}{\texttt{TP}+\texttt{FN}}+\frac{ \texttt{TN}}{\texttt{TN}+\texttt{FP}}). \tag{27}\]  
where  
* \(FP\), false positives, the number of images belonging to the class but incorrectly labeled as not. * \(FN\), false negatives, the number of images belonging to the class but incorrectly labeled as not. * \(TP\), true positive, the number of images belonging to the class and correctly labeled as such. * \(TN\), true negative, the number of images not belonging to the class and correctly labeled as such. The area under the receiver operating characteristic curve (ROC-AUC) is a useful and broadly used measure to evaluate the performance of binary classifiers. Recently [46], it has been extended to multi-class classifiers, where for each class, the other classes are simply lumped together as the second class.