## 5 Discussions
The significance is described in Table 8. The combined dataset's performance is compared with three individual datasets (APTOS, EyePACS, and ODIR) using four evaluation metrics (recall, precision, specificity, and accuracy) for DR classification tasks. The results show no significant difference in performance for any matrices between the combined and APTOS datasets. However, there is a significant difference between the combined dataset and the EyePACS or ODIR datasets. Overall, these results suggest that the combined dataset performs differently than the EyePACS and ODIR datasets but is like the APTOS dataset. The study also concludes that combining multiple datasets improves performance compared to individual datasets alone. Plotting the ROC (Receiver Operating Characteristic) Curve is a reliable method of assessing the classification accuracy of a classifier. By charting the True Positive Rate (TPR) and the False Positive Rate (FPR), we can see how the classifier behaves for each threshold. The better the model does in categorizing the data, the closer the ROC curve touches the upper left corner of the figure. To determine how much of the plot lies beneath the curve, we may compute the AUC (area under the curve). The model is better the closer AUC gets to 1 [77]. The AUC score and the ROC curve of the combined dataset evaluated on four models are illustrated in Figure 4. The black diagonal dashed line represent the 50%  
Figure 3: The training and validation loss for four datasets with four pre-trained models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Recall** & **Precision** & **Specificity** & **Accuracy** \\ \hline APTOS-Combined & 0.397 & 0.876 & 0.189 & 0.389 \\ EyePACS-Combined & 0.048 & 0.008 & 0.014 & 0.004 \\ ODIR-combined & 0.001 & 0.000 & 0.002 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The _p_-value of the combined dataset with an individual dataset based on a paired _t_-test. The significant value (alpha value) is set to 0.05.  
area. From the plot, the combined model shows a better classification of DR and normal retinal images with the DenseNet121 model with an AUC score of 0.9947. The AUC score of MobileNetV2 is 0.9897, VGG16 is 0.9608, and InceptionV3 is 0.9781. The AUC score of the other three datasets is depicted in Figure 5, the diagonal black dashed line represents the 50% area. The ODIR shows the least performance, and the AP-TOS has a similar performance to that of the combined (also concluded from the statistical analysis in Table 8). When using DenseNet121, the APTOS has a higher AUC score of 0.9902, and EyePACS has a higher AUC score of 0.9320. With the MobileNetV2 model, ODIR has a score of 0.7278 for AUC. Figure 4: The ROC curve of the four different models evaluated on the combined dataset. Figure 5: The ROC curve of the four different models evaluated on three datasets. (**a**) APTOS, (**b**) EyePACS, and (**c**) ODIR. The comparison with previous works is depicted in Table 9. The transfer learning methods used in this study achieved better performance when compared to related works with binary and multi-classification. Evaluating models on multiple datasets can provide insight into how well they generalize to new and diverse data. Each dataset may have biases that might influence the overall findings. Consequently, merging the datasets that were publicly available from people of varying ethnicities is a potential solution to the issue of producing biased results. This information may be used to decide which model is appropriate for a particular task or to pinpoint areas that want development.