## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Model 2**} \\ \hline  & \multicolumn{3}{c}{**Model 2-1**} & \multicolumn{6}{c}{**Model 2-2**} & \multicolumn{6}{c}{**Model 2-3**} \\ \hline
**Level of DR** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline
0 & 0.78 & 0.94 & 0.85 & 0.76 & 0.96 & 0.85 & 0.9 & 0.98 & 0.94 \\ \hline
1 & 0.19 & 0.02 & 0.04 & 0.12 & 0.01 & 0.02 & 0.92 & 0.72 & 0.81 \\ \hline
2 & 0.37 & 0.23 & 0.28 & 0.36 & 0.12 & 0.18 & 0.85 & 0.76 & 0.8 \\ \hline
3 & 0.25 & 0.08 & 0.13 & 0.27 & 0.06 & 0.1 & 0.88 & 0.31 & 0.46 \\ \hline
4 & 0.47 & 0.18 & 0.26 & 0.5 & 0.11 & 0.18 & 1 & 0.18 & 0.3 \\ \hline
**Accuracy** & & 73.25\% & \multicolumn{6}{c}{73.30\%} & \multicolumn{6}{c}{**90.60\%**} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance metrics comparison for Model 2 and sub-models. Figure 9: The confusion matrix of Model 2-3. Upon completing the training of the three models, we conducted a comprehensive comparative analysis to evaluate their performance based on specific metrics such as validation accuracy and loss values. Model 1 utilized grayscale images with data augmentation and background removal, achieving a validation accuracy of 90.39% and a loss of 0.2560. This indicates that converting images to grayscale effectively reduces computational complexity while retaining essential features necessary for accurate classification. Model 2, employing color images with the same augmentation and background removal techniques, slightly outperformed Model 1 with a validation accuracy of 90.60% and a lower loss of 0.2450. The inclusion of color information provided additional features that enhanced the model's ability to distinguish between different stages of diabetic retinopathy. In contrast, Model 3, which was identical to Model 2 in preprocessing but trained with fewer epochs (40 instead of 200), showed a decreased validation accuracy of 87.64% and a higher loss of 0.2684. The reduced performance of Model 3 underscores the importance of sufficient training epochs to allow the model to fully learn and generalize complex patterns associated with diabetic retinopathy. These findings suggest that while both grayscale and color images can be effectively used alongside data augmentation and background removal to enhance model performance, adequate training duration is crucial for optimal results. Therefore, Model 2 is most suitable when the highest accuracy is required, leveraging the richness of color information and thorough training. Model 1  
Figure 10: The confusion matrix of Model 3-3. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Model 3**} \\ \hline  & \multicolumn{3}{c}{**Model 3-1**} & \multicolumn{3}{c}{**Model 3-2**} & \multicolumn{3}{c}{**Model 3-3**} \\ \hline
**Level of DR** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline
0 & 0.75 & 0.96 & 0.85 & 0.76 & 0.93 & 0.84 & 0.88 & 0.98 & 0.93 \\ \hline
1 & 0.19 & 0.01 & 0.01 & 0.13 & 0.02 & 0.04 & 0.9 & 0.6 & 0.72 \\ \hline
2 & 0.32 & 0.11 & 0.17 & 0.29 & 0.15 & 0.2 & 0.82 & 0.7 & 0.76 \\ \hline
3 & 0.4 & 0.04 & 0.07 & 0.3 & 0.04 & 0.07 & 0.8 & 0.28 & 0.41 \\ \hline
4 & 0.73 & 0.05 & 0.1 & 0.41 & 0.07 & 0.12 & 0.86 & 0.16 & 0.27 \\ \hline
**Accuracy** & & 73.02\% & & & 71.44\% & & & **87.64\%** & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance metrics comparison for Model 3 and sub-models. offers a balance between accuracy and computational efficiency, making it suitable for situations with limited resources. Model 3 might be appropriate when training time or computational power is constrained, accepting a trade-off in accuracy for faster deployment. Table 6 presents a summary of the models' performances.