## 6 Conclusions  
This study investigated the performance of four distinct transfer learning models with each of the four separate data sets. Using the datasets (APTOS, EyePACS, ODIR, and the combined) with preprocessing and data augmentation approaches, we got encouraging findings on our DR classification model in this study. Pretrained models for classifying DR classes include VGG16, InceptionV3, DenseNet121, and MobileNetV2. The APTOS data has the highest accuracy and recall (98.50%) with MobileNetV2 and DenseNet121 models. The test results of EyePACS showed that DenseNet121 has the highest accuracy and recall of 89.10%, while ODIR shows 75.82% accuracy with DenseNet121 and 0.7743 recall value with VGG16. In the combined dataset the DenseNet121 model outperforms other models with high accuracy, recall, specificity, and precision (98.97%). The statistical results suggest that the combined dataset performs differently than the EyePACS and ODIR datasets (\(p\)-value less than 0.05) but is like the APTOS dataset (\(p\)-value greater than 0.05). Using pre-trained models and transfer learning can increase the effectiveness and accuracy of DL models while requiring less training data and resources. It can be particularly beneficial in applications such as medical diagnosis. The established approach can be widely adopted to accommodate additional screenings for diabetic patients performed by clinics to identify DR and refer them to an ophthalmologist to begin the right course of treatment before it progresses into blindness.  
Even though the transfer learning strategy described in this work for the categorization of DR has shown some encouraging findings, there are limitations to the work that needs to be taken into consideration. The study used only a small dataset size and evaluated  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Dataset** & **Model** & **Label Count** & **Result** \\ \hline
[39] & Kaggle & Grid search CNN & 5 & 89\% \\
[41] & ODIR, IDRiD & Vision transformer & 6 & 82.35\% \\
[40] & DARETDB1 & ResNext + DenseNet & 2 & 96.98\% \\
[33] & APTOS & AlexNet, ResNet-101 with ACS & 2 & 93\% \\
[15] & IDRiD & VGG16 & 5 & 77.6\% \\
[17] & EyePACS & Efficient-b3 & 5 & 92\% \\
[42] & Train EyePACS, Test APTOS & Source-Free Transfer Learning & 5 & 91.2\% \\
[9] & APTOS & CNN & 2 & 95.3\% \\
[20] & DDR & VGG16 & 5 & 100\% \\
[78] & APTOS & Few-Shot Learning & 2 & 99.73\% \\
[79] & EyePACS & VGGNet & 5 & 96.6\% \\
[31] & DDR, EyePACS, and IDRiD & VGG16 & 5 & 85.4\% (DDR) \\
[47] & Messidor-2, EyePACS, & & & 77.9\% (EyePACS) \\  & DIARETDB0 & CNN + SVD + Inception-V3 & 5 & 94.59\% (Messidor-2) 97.92\% (EyePACS) \\  & & & & 93.52\% (DIARETDB0) \\
[50] & Own dataset & DenseNet-121 + CNN & 2 & 84.47\% \\
[80] & EyePACS & VGG16 & 2 & 75.7\% \\
[81] & Own dataset & VGG16 & 3 & 87.28\% \\
[76] & APTOS + EyePACS + & MobileNetV2 & 5 & 91.68\% \\  & Messidor & VGGNet-s & 5 & 95.68\% \\
[82] & EyePACS + APTOS + ODIR & DenseNet121 & 2 & 98.97\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison with other works related to deep learning models in terms of accuracy.  
balanced data. Also, it is not clinically validated. The study may be expanded (as a future enhancement) to incorporate more datasets and models from many new ethnic groups and nations. Also, the model can be deployed into a mobile application as a future enhancement to test with actual patient data.  
Conceptualization, A.M.M.; data curation, S.A.; methodology, A.M.M., S.A. and S.S.; software, S.A.; writing--original draft preparation, S.S.; writing--review and editing, A.M.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Kuwait University Research Grant No. EO04/18.  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [55; 57; 58].  
The authors would like to thank Kuwait University research sector for the continuous support.  
The authors declare no conflict of interest.