## 3 Results and Discussions
Hence, the models will exhibit bias towards class 0 when they are applied to the original APTOS dataset, whereas for the augmented APTOS dataset, the proportion is nearly the same, so comparing the overall testing accuracy between the two datasets is not recommended. To address this issue, the class-wise accuracy was calculated for the three datasets and plotted as shown in Figure 11.  
A closer study of the plots in Figure 11 shows that the three models were able to predict class 0, \"No DR\" cases, quite easily for both the original and augmented APTOS datasets; however, only the ResNet50 model was able to classify \"No DR\" cases for the Mauritius dataset. This is to be expected since class 0 is quite distinct from the other classes given the absence of DR features such as microaneurysms. For the VGG16 model, class 3 was the one that achieved the lowest accuracy out of all three datasets with none of the 55 cases being correctly classified for the original APTOS dataset. We also noted that none of the cases of class 1 for the Mauritian dataset were correctly identified. This shows that the model was unable to learn to distinguish the features of these two classes. A closer look at the results obtained shows that most of the  
Figure 11: Detailed testing accuracy for each class for the 3 datasets and the 3 models. cases for class 3 were misclassified as class 2 and a few cases as classes 1 and 4. Class 3 represents the moderate cases, which fall between the mild and proliferative cases and therefore may be difficult to identify. There may be intraretinal haemorrhage, which also complicates the task. For the ResNet50 model, classes 1, 3 and 4 were the most difficult to classify for the original dataset, classes 1 and 3 were the most difficult to classify for the augmented dataset, and class 4 was the most difficult class to classify for the Mauritian dataset. The difficulty in the classification of class 4 for the Mauritian cohort may be due to choroidal fronts and troughs being more pronounced in the local dataset due to presence of pigments. This is due to the local population having different skin colours. For the DensetNet169 model, the results obtained for the three datasets are variable with classes 1 and 4 being the less distinctive for the original dataset, classes 1 and 3 being less distinctive for the augmented dataset and classes 1, 2 and 3 being less distinctive for the Mauritian dataset. Here, none of the 202 cases for classes 1 and 4 in the original APTOS dataset were correctly identified. A closer look at the class-wise results shows that most of the images from class 1 were wrongly classified as class 2, and a few were classified as classes 0 and 3. Similarly, for class 4, we found that most of the images from class 4 were wrongly classified as class 2 and the rest as class 3. Based on these results, we concluded that for the APTOS dataset, classes 1 and 3 were the most difficult to learn. Although none of the models had been trained with the data from Mauritius, the ResNet50 model achieved quite good results on this blind test dataset, achieving accuracies of 60% and above. It also obtained the best results compared to the other two models. This can be explained by the fact that the Densenet169 has more layers and may be overlearning and therefore generalizing less. Resnet50 has residual connections between layers, meaning that the output of a layer is a convolution of its input plus its input. It is also deeper than VGG16 with fewer parameters and is better able to identify the features to distinguish between the different classes of diabetic retinopathy. Moreover, although ResNet is much deeper than VGG16, the model size is substantially smaller due to the use of global average pooling rather than fully connected layers.