## 5 Discussions  
The data type plays a crucial role and significantly influences the outcomes. In this study, different datasets are combined regardless of the patient's origin and are trained on four different pre-trained models, namely VGG16, InceptionV3, MobilenetV2, and DenseNet121. The system had undergone proof-of-concept development on a relatively limited scale. It may be expanded (as a future enhancement) to incorporate more datasets and models from many new ethnic groups and nations. Many researchers have used pretrained transfer learning models to predict and classify DR retinal images. Almost all works are trained on a single dataset. The VGG16, Densenet121, InceptionV3, and MobileNetV2 models are recognized for effective use as a CNN model for DR classification. [PERSON] et al. [20] show an accuracy of 100% in classifying DR with the VGG16 model. With five classes in DR, [PERSON] et al. [31] attained an accuracy of 89.8% and 90.2% for six categories. [PERSON] et al. [47] trained InceptionV3 with three different datasets and achieved an accuracy of 97.92% with EyePACS, 94.59% in Messidor-2, and 93.52% with the DIARETDB0 dataset. [PERSON] and [PERSON] [54] showed an accuracy of 90.9% with a binary classification of DR with the InceptionV3 model. [PERSON] et al. [74] achieved an accuracy of 95% with the DenseNet121 model for the APTOS dataset in classifying the DR images. [PERSON] et al. investigated the subject of grading DR [75] using MobileNetV1 and MobileNetV2 as well as other transfer learning techniques. Using MobileNetV2, they could classify with 78.1% accuracy, and using MobileNetV1, only 58.3% accuracy. [PERSON]. [76] generated a model with the MobileNetV2 employing a dataset combined by EyePACS, APTOS, and Messidor2 and achieved an accuracy of 91.68%. Figure 3 depicts the training and validation loss of this study. The APTOS model performs very well, with the DenseNet121 and MobileNetV2 models, with an accuracy of 98.5% and recall of 0.985. There is no massive difference in the loss value of these two models. For the EyePACS dataset, all the models perform better regarding loss value. The evaluation metrics show the DenseNet121 model with an accuracy and recall of 0.891. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Recall** & **Precision** & **Specificity** & **Accuracy** & **Train Loss** & **Valid Loss** \\ \hline MobileNetV2 & 0.7500 & 0.7500 & 0.7500 & 0.7500 & 0.5072 & 0.5938 \\ DenseNet121 & 0.7582 & 0.7582 & 0.7582 & 0.7582 & 0.4888 & 0.5601 \\ InceptionV3 & 0.6739 & 0.6963 & 0.6705 & 0.6723 & 0.5453 & 0.6193 \\ VGG16 & 0.7743 & 0.7151 & 0.6543 & 0.7177 & 0.5843 & 0.5480 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: ODIR model evaluation metrics results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Recall** & **Precision** & **Specificity** & **Accuracy** & **Train Loss** & **Valid Loss** \\ \hline MobileNetV2 & 0.9851 & 0.9851 & 0.9926 & 0.9851 & 0.0109 & 0.0518 \\ DenseNet121 & 0.9897 & 0.9897 & 0.9948 & 0.9897 & 0.0051 & 0.0368 \\ InceptionV3 & 0.9715 & 0.9727 & 0.9864 & 0.9721 & 0.0523 & 0.0989 \\ VGG16 & 0.9879 & 0.9879 & 0.9939 & 0.9879 & 0.0490 & 0.0465 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The model evaluation metrics results for the combined dataset. Similar is the loss value results for the ODIR dataset. However, VGG16 offers a recall value of 0.7743 and the DenseNet121 with an accuracy of 75.82%. The combined dataset has all models with better training and validation loss. The DenseNet121 model has the highest recall, precision, and accuracy scores, all of which are 0.9897, which is high compared to Sheikh S.O. [76]. The paired _t_-test compares each model's performance on the combined dataset with their performance on three independent datasets. The alpha or the significant value is set to 0.05. There is a statistically significant difference between the evaluation metrics of the two datasets if the assessed _p_-value is less than 0.05. Hence, the null hypothesis is rejected.