## 4 Discussion  
The results obtained in this study demonstrate the potential of deep learning models in the automated diagnosis of diabetic retinopathy (DR). Through the use of grayscale and normal image data in conjunction with data augmentation and background removal techniques, the models' performances were significantly enhanced.  
Model 1, using grayscale images, achieved a validation accuracy of 90.39%. The removal of color information likely helped the model focus on critical retinal features, improving accuracy.  
Model 2, trained with normal RGB images, slightly outperformed Model 1 with an accuracy of 90.60%. This marginal improvement may be due to the additional features provided by color information, although it increased the model's complexity and loss.  
Model 3, trained with fewer epochs, exhibited a lower accuracy of 87.64%, showing the importance of adequate training duration. This model still achieved satisfactory results, suggesting that in time-constrained environments, fewer training epochs can be an acceptable compromise, though performance is slightly reduced.  
Comparing our results with previous studies highlights the effectiveness of our proposed models. Our models outperform several recent works, such as [PERSON] et al. (2019), who reported an 86.10% accuracy using EfficientNet B1 on the APTOS dataset; [PERSON] et al. (2019), who achieved 85% accuracy using EfficientNet B3 and SHAP; and [PERSON] et al. (2019), who obtained 71.85% accuracy with a Discrete Wavelet Transform combined with a CNN approach. Although [PERSON] et al. (2020) achieved a slightly higher accuracy of 92.90% using ResNet50 on the Kaggle dataset, our models are notably more compact in size. This demonstrates that our grayscale and augmentation techniques not only enhance performance but also contribute to creating smaller, more efficient models.  
However, it is essential to consider the generalizability of these results. The models were trained on the EyePACS dataset, which may not represent the full diversity of retinal images from different geographic locations or medical institutions. Testing on datasets like Messidor or APTOS, or data collected from local hospitals, could further validate these findings and ensure robustness across varying populations. Additionally, exploring other preprocessing techniques and architectures might enhance the model's capability even further.  
Our study demonstrates the effectiveness of our deep learning models in aiding the diagnosis of diabetic retinopathy (DR). By achieving high accuracy with a compact model size, we offer a practical solution that can enhance diagnostic accuracy in clinical settings,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Detailed** & **Epochs** & **Validation Accuracy** \\ \hline \multirow{3}{*}{1} & grayscale images & 200 & 70.86\% \\ \cline{2-4}  & data augmentation & 200 & 70.64\% \\ \cline{2-4}  & removed backgrounds & 10 & **90.39\%** \\ \hline \multirow{3}{*}{2} & normal picture & 200 & 73.25\% \\ \cline{2-4}  & data augmentation & 200 & 73.30\% \\ \cline{2-4}  & removed backgrounds & 10 & **90.60\%** \\ \hline \multirow{3}{*}{3} & normal picture & 40 & 73.02\% \\ \cline{2-4}  & data augmentation & 40 & 71.44\% \\ \cline{2-4}  & removed backgrounds & 10 & **87.64\%** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Validation accuracy of different models and techniques in image processing.  
especially in resource-constrained environments. This advancement highlights the strength of our approach and contributes significantly to the field by providing an efficient tool for early DR detection. While further research is needed to optimize performance and confirm applicability across diverse populations, our work represents a meaningful step forward in integrating AI tools into healthcare. It is important to view these models as supportive aids for healthcare professionals rather than replacements, as human expertise remains essential in medical diagnoses.