## 4 Experimental Validation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Labels** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Score** \\ \hline \hline \multicolumn{5}{c}{**Entire Dataset**} \\ \hline Normal & 92.22 & 89.58 & 95.29 & 89.25 & 92.35 \\ \hline Mild & 96.86 & 88.06 & 79.73 & 98.79 & 83.69 \\ \hline Moderate & 96.59 & 93.53 & 94.00 & 97.56 & 93.77 \\ \hline Severe & 97.00 & 84.00 & 53.85 & 99.42 & 65.62 \\ \hline Proliferative & 95.77 & 75.00 & 71.19 & 97.92 & 73.04 \\ \hline
**Average** & **95.69** & **86.04** & **78.81** & **96.59** & **81.69** \\ \hline \multicolumn{5}{c}{**Training Phase (70\%)**} \\ \hline Normal & 91.62 & 89.01 & 94.92 & 88.33 & 91.87 \\ \hline Mild & 96.49 & 89.58 & 76.79 & 98.91 & 82.69 \\ \hline Moderate & 96.88 & 93.62 & 94.96 & 97.59 & 94.29 \\ \hline Severe & 97.08 & 92.31 & 46.15 & 99.79 & 61.54 \\ \hline Proliferative & 95.71 & 68.42 & 72.22 & 97.48 & 70.27 \\ \hline
**Average** & **95.56** & **86.59** & **77.01** & **96.42** & **80.13** \\ \hline \multicolumn{5}{c}{**Testing Phase (30\%)**} \\ \hline Normal & 93.64 & 90.99 & 96.19 & 91.30 & 93.52 \\ \hline Mild & 97.73 & 84.21 & 88.89 & 98.51 & 86.49 \\ \hline Moderate & 95.91 & 93.33 & 91.80 & 97.48 & 92.56 \\ \hline Severe & 96.82 & 75.00 & 69.23 & 98.55 & 72.00 \\ \hline Proliferative & 95.91 & 88.89 & 69.57 & 98.98 & 78.05 \\ \hline
**Average** & **96.00** & **86.48** & **83.14** & **96.97** & **84.52** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result analysis of the XAITO-DRGC approach with various measures on the APTOS 2019 dataset. The training accuracy (TA) and validation accuracy (VA) acquired by the XAITO-DRGC method on the APTOS 2019 dataset is demonstrated in Figure 5. The experimental outcome denoted that the XAITO-DRGC algorithm has reached maximal values of TA and VA. In particular, the VA is greater than TA. The training loss (TL) and validation loss (VL) gained by the XAITO-DRGC methodology on the APTOS 2019 dataset are established in Figure 6. The experimental outcome implied that the XAITO-DRGC method has accomplished minimal values of TL and VL. Explicitly, the VL is lesser than TL. Figure 5: TA and VA analysis of the XAITO-DRGC approach under the APTOS 2019 dataset. Figure 6: TL and VL analysis of the XAITO-DRGC approach under the APTOS 2019 dataset. To establish the enhanced performance of the XAITO-DRGC model, a comparison study is made on the APTOS 2019 dataset in Table 3 and Figure 7[25]. The results implied that the CNN299 method has shown lower classification performance. At the same time, the CNN512, CN299-dropout, EfficientNetB0, and EfficientNetB0-dropout models have demonstrated moderately improved classifier results. Moreover, the CNN512-dropout model has reached reasonable performance with \(accu_{y}\) of 88.60%, \(sens_{y}\) of 81.56%, and \(spec_{y}\) of 95.10%. However, the XAITO-DRGC model has outperformed other models with a maximum \(accu_{y}\) of 96.00%, \(sens_{y}\) of 83.14%, and \(spec_{y}\) of 96.97%. Figure 8 signifies the confusion matrices given by the XAITO-DRGC method on the DDR dataset. On the entire dataset, the XAITO-DRGC algorithm has identified 1225 samples as normal, 105 samples as mild, 867 samples as moderate, 31 samples as severe, and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline XAITO-DRGC & 96.00 & 83.14 & 96.97 \\ \hline CNN299 & 80.00 & 81.54 & 81.51 \\ \hline CNN299-dropout & 83.30 & 82.37 & 84.81 \\ \hline CNN512 & 85.80 & 80.80 & 95.30 \\ \hline CNN512-dropout & 88.60 & 81.56 & 95.10 \\ \hline EfficientNetB0 & 82.30 & 82.80 & 88.07 \\ \hline EfficientNetB0-dropout & 82.20 & 81.13 & 83.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of the XAITO-DRGC approach with recent methods on the APTOS 2019 dataset. Figure 7: Comparative analysis of the XAITO-DRGC approach under the APTOS 2019 dataset. 159 samples as proliferative.