## 4 Conclusions  
In this work, transfer learning was applied at multiple levels with the aim of training multiple models to classify diabetic retinopathy for a completely blind dataset, the Mauritian cohort. At the initial stage, transfer learning was performed with three general pre-trained models, VGG16, ResNet50 and DenseNet169, using the APTOS dataset for diabetic retinopathy. Even after fine-tuning the three models, some classes were not being classified, and accuracies were not very high. This could be due to the dataset being highly imbalanced with almost 50% of the dataset belonging to \"No DR\" cases and the remaining 50% being distributed amongst the four DR classes. Hence, the dataset was augmented to achieve a comparable number of cases in each of the classes. Transfer learning was performed on the augmented APTOS dataset, and a better performance was achieved in the various experiments. It was found that the ResNet50 model produced equivalent or better results for all the classes compared to the VGG16 and DenseNet169 models. These trained enhanced models were then applied to the blind Mauritian dataset, and the results obtained are compared to the annotated local images. Again, the ResNet50, given its architecture, achieved the best results amongst the three models, and the accuracies obtained were very good. Class 0 achieved accuracies of 98%, 95% and 96% for the original APTOS dataset, \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Authors** & **Techniques Used** & **Discussions** \\ \hline [PERSON] et al. (2017) & Model: pre-trained Inception V3 model & Performance: accuracy—90.9\%, loss—3.94\% \\ [PERSON] et al. (2017) & Dataset: APTOS dataset & Limitation: number of classes is limited to DR and No DR only \\  & Number of images: 2500 images (1250 for NoDR and 1250 for DR) & \\ \hline  & Model: deep learning hybrid model with pre-trained Inception-ResNet-v2 as a base model & Performance: accuracy—72.33\% (Messidor-1), 82.18\% (APTOS dataset) & \\ [PERSON] et al. (2012) & Dataset: Messidor-1 and APTOS dataset & Limitation: did not check whether model was overfitting \\  & Number of images: 1200 images (Messidor-1), 3662 images (APTOS) & Performance: sensitivity—90\%, specificity—90\%, AUC—95\% \\ \hline  & Model: pre-trained Inception V3 model & Performance: sensitivity—90\%, specificity—90\%, AUC—95\% \\ [PERSON] et al. (2014) & Dataset: DR dataset obtained from VisionQuest Biomedical database & Limitation: results for No DR, MildDR, Moderate DR were 47\%, 50\% and 35\% \\  & Number of images: 6805 images & Performance: accuracy—74\%, sensitivity—80\%, a specificity—65\%, AUC—80\% Limitation: low accuracy compared to similar experimentations \\ \hline  & Model: Fine-tuned and pre-trained ResNet50, VGG16, DenseNet169 models & Performance: accuracy (ResNet50)—82\% (APTOS dataset), 79\% (Mauritian dataset) & \\ Our proposed Model & Dataset: APTOS dataset, Mauritian dataset & Novelty: performed multiclass classification (5 different classes) for Mauritian dataset \\  & Number of images: 3662 images (APTOS), 208 images (Mauritius) & \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._the augmented APTOS dataset and the Mauritian dataset, respectively, clearly indicating that the model is able to easily distinguish this class from the other classes, thus confirming the potential of training a precursor model for class 0 versus others. It was observed that some classes performed much better than others, and this needs to be further investigated. Classes 1, 2 and 3 achieved acceptable performances while class 4 was the most difficult to classify. The diabetic retinopathy expert observed that class 3 was graded more precisely. Moreover, retinal images with pronounced choroidal fonds seemed to be identified as class 4 by the software, which clinically rates as normal variants. This is an unexpected behaviour of class 4, representing a major difference between the training APTOS data and the Mauritius data.