## 3 Results and Discussions
Based on the results of the ResNet50 model, the results were further investigated, and a confusion matrix of the predicted vs. actual results was plotted, as shown in Figure 12.  
The precision, recall and F1 score were computed for each individual class and are displayed in Table 2. Additionally, the weighted average was also calculated. Figure 12: Confusion matrix for Mauritian data classified by ResNet50. From the confusion matrix, we found that very good accuracies were achieved for all the classes, and the cases that were wrongly classified were close to the diagonal, being either from the class just before or just after. Thus, for class 0, the cases that were wrongly classified were actually from classes 1 and 2, for class 1, they were from classes 0 and 2, for class 2, they were from classes 1 and 3 with few cases from class 4, and for class 3, they were from class 4. This behaviour is not followed by class 4, where the wrongly classified classes were from all classes with the majority from class 2, which is quite far from class 4. Class 4 is of interest and requires further investigation. A comparison of our proposed model with the other available works in DR classification is given in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1 Score** \\ \hline Class 0 & 0.9600 & 0.8889 & 0.9231 \\ Class 1 & 0.8600 & 0.6935 & 0.7679 \\ Class 2 & 0.7551 & 0.8222 & 0.7872 \\ Class 3 & 0.7778 & 0.5000 & 0.6087 \\ Class 4 & 0.6000 & 0.9091 & 0.7229 \\ Weighted Average & 0.8165 & 0.7933 & 0.7945 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics for Mauritian data classified by ResNet50. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Authors** & **Techniques Used** & **Discussions** \\ \hline \multirow{4}{*}{[PERSON] et al. [23]} & Model: deep model based on ResNet & Pre-trained models (ResNet and R-CNN) were \\  & Dataset: Shanghai Integrated Diabetes Prevention & used. ROC was used to evaluate performance. \\  & and Care System (Shanghai Integration Model, & Performance: AUC scores of 0.943, 0.955, 0.960 \\  & SIM) between 2014 and 2017 & and 0.972 for mild, moderate, severe and \\  & Number of images: 666,383 images & proliferative cases were achieved, showing good \\  & & performance using transfer learning \\ \hline \multirow{4}{*}{[PERSON] et al. [11]} & Model: pre-trained Inception V3 model & \\  & Dataset: Eye-PACS dataset & \\  & Number of images: 3908 images (800 from each & \\  & class except 708 from class 4) & \\ \hline \multirow{4}{*}{[PERSON] et al. [12]} & Model: different pre-trained networks such as & \multirow{4}{*}{\begin{tabular}{l} Performance: accuracy--48.2\%, limitations: \\ low accuracy \\ \end{tabular} } \\  & AlexNet, VGG-S, VGG16 and VGG19 Dataset: the & \begin{tabular}{l} Performance: best area under the curve (AUC) \\ (VGG-S)—98.34\% (Messidor dataset), 97.86\% \\ (DR1 dataset) \\ \end{tabular} \\  & \begin{tabular}{l} Number of images: 1014 images (DR1), 1200 \\ images (Messidor) \\ \end{tabular} & \begin{tabular}{l} Limitations: number of classes is limited to DR \\ and No DR only \\ \end{tabular} \\ \hline \multirow{4}{*}{[PERSON] et al. [13]} & Model: developed a deep All-CNN architecture & \multirow{4}{*}{\begin{tabular}{l} Performance: accuracy--86.64\%, loss--0.46, \\ average F1 score--0.6318 \\ Limitation: no detailed information \\ on overfitting \\ \end{tabular} } \\  & \
\begin{tabular}{l} Number of images: 35,126 images \\ \end{tabular} & &
\begin{tabular}{l} Performance: best accuracy (AlexNet)—97.9\% \\ Limitation: high computational power needed \\ (Intel Xeon E5-2620 processor (2 GHz), 96 GB of \\ RAM) since the model needed to train on \\ 14,648 images. Additionally, no detailed \\ information was given for model overfitting \\ during the training phase. The only method used to counter overfitting was data augmentation, \\ which takes place before the model \\ training phase. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison table of similar work.