## 4 Experimental Validation
Furthermore, on 70% of TR data, the XAITO-DRGC technique has identified 853 samples as normal, 76 samples as mild, 606 samples as moderate, 22 samples as severe, and 112 samples as proliferative. Additionally, on 30% of TS data, the XAITO-DRGC methodology has identified 372 samples as normal, 29 samples as mild, 261 samples as moderate, 9 samples as severe, and 47 samples as proliferative. Table 4 and Figure 9 present an overall classification output of the XAITO-DRGC technique on the DDR dataset. The experimental values notified that the XAITO-DRGC method has shown enhanced results under distinct aspects. For example, with the entire dataset, the XAITO-DRGC method has rendered an average \(accu_{y}\) of 98.15%, \(prec_{n}\) of 92.13%, \(rec_{1}\) of 86.26%, \(spec_{y}\) of 98.55%, and \(F_{score}\) of 88.78%. At the same time, with 70% of TR data, the XAITO-DRGC technique has offered an average \(accu_{y}\) of 98.11%, \(prec_{n}\) of 93.89%, \(rec_{n}\) of 85.90%, \(spec_{y}\) of 98.45%, and \(F_{score}\) of 89.31%. Finally, with 30% of TS data,  
Figure 8: Confusion matrices of the XAITO-DRGC approach under the DDR dataset (**a**) entire dataset, (**b**) 70% of TR data, and (**c**) 30% of TS data. the XAITO-DRGC methodology has rendered an average \(accu_{y}\) of 98.24%, \(prec_{n}\) of 88.32%, \(rec_{l}\) of 87.30%, \(spec_{y}\) of 98.77%, and \(F_{score}\) of 87.54%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Labels** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** & **F-Score** \\ \hline \multicolumn{6}{c}{**Entire Dataset**} \\ \hline Normal & 97.12 & 96.53 & 97.77 & 96.48 & 97.15 \\ \hline Mild & 98.56 & 87.50 & 83.33 & 99.37 & 85.37 \\ \hline Moderate & 97.48 & 96.12 & 96.87 & 97.82 & 96.49 \\ \hline Severe & 99.24 & 91.18 & 65.96 & 99.88 & 76.54 \\ \hline Proliferative & 98.32 & 89.33 & 87.36 & 99.18 & 88.33 \\ \hline \multicolumn{6}{c}{**Average**} & **98.15** & **92.13** & **86.26** & **98.55** & **88.78** \\ \hline \multicolumn{6}{c}{**Training Phase (70\%)**} \\ \hline Normal & 96.86 & 95.95 & 97.82 & 95.91 & 96.88 \\ \hline Mild & 98.57 & 90.48 & 81.72 & 99.52 & 85.88 \\ \hline Moderate & 97.37 & 95.58 & 97.12 & 97.52 & 96.34 \\ \hline Severe & 99.32 & 95.65 & 66.67 & 99.94 & 78.57 \\ \hline Proliferative & 98.40 & 91.80 & 86.15 & 99.38 & 88.89 \\ \hline \multicolumn{6}{c}{**Average**} & **98.11** & **93.89** & **85.90** & **98.45** & **89.31** \\ \hline \multicolumn{6}{c}{**Testing Phase (30\%)**} \\ \hline Normal & 97.74 & 97.89 & 97.64 & 97.84 & 97.77 \\ \hline Mild & 98.54 & 80.56 & 87.88 & 99.03 & 84.06 \\ \hline Moderate & 97.74 & 97.39 & 96.31 & 98.54 & 96.85 \\ \hline Severe & 99.07 & 81.82 & 64.29 & 99.73 & 72.00 \\ \hline Proliferative & 98.14 & 83.93 & 90.38 & 98.71 & 87.04 \\ \hline \multicolumn{6}{c}{**Average**} & **98.24** & **88.32** & **87.30** & **98.77** & **87.54** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result analysis of the XAITO-DRGC approach with various measures on the DDR dataset. Figure 9: Result analysis of the XAITO-DRGC approach under the DDR dataset. The TA and VA acquired by the XAITO-DRGC technique on the DDR dataset are shown in Figure 10. The experimental outcome denoted the XAITO-DRGC algorithm has reached maximal values of TA and VA. In particular, the VA is greater than TA. The TL and VL attained by the XAITO-DRGC method on the DDR dataset are established in Figure 11. The experimental outcome represented the XAITO-DRGC approach has accomplished minimal values of TL and VL. Specifically, the VL is lesser than TL. Figure 11: TL and VL analysis of the XAITO-DRGC approach under the DDR dataset. Figure 10: TA and VA analysis of the XAITO-DRGC approach under the DDR dataset. To establish the enhanced performance of the XAITO-DRGC method, a comparative study is made on the DDR dataset in Table 5 and Figure 12. The results represented that the CNN299 method displayed lower classification performance.