## 5 Paper Review  
This section discusses the selected papers based on different aspects like the architecture used, the target dataset used, the optimizer used, and the LR used, the performance of the architecture \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Size** & **Grades** \\ \hline Kaggle & 88,000 & 5 \\ \hline Messidor & 1200 & 4 \\ \hline DR1 & 1014 & 2 \\ \hline E-ophtha & 463 & 2 \\ \hline STARE & 397 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Diabetic retinopathy (DR) datasets description. after transfer learning, the fine-tuning process employed, and, finally, the validation process whenever applicable. In transfer learning, a set of weights that were learned from an image dataset can be used to classify another image dataset. The deep layers are generic and can be used to extract salient features that are suitable for classifying any image. This aspect is why many authors have tried to use transfer learning in detecting DR. For instance, [PERSON] et al. [53] used InceptionV3 architecture to classify DR into two grades: DR or No DR. The dataset that was considered by the authors contained 128,175 images. The reported results on two test datasets, with sizes 9963 and 1748, had sensitivities of 97.5% and 96.1%, respectively. [PERSON] et al. [54] used the Kaggle dataset to assess the performance of the InceptionV3 model to classify DR into five grades. The authors chose 4000 images and cropped them to 500 pixels. The authors used accuracy to assess the model's performance, which was reported as 48.8%. [PERSON] et al. [55] discussed the usage of transfer learning for detecting DR by comparing different network architectures, including AlexNet, VGG-S, VGG16, and VGG19, to two datasets: the Messidor and DR1 datasets. Three transfer learning techniques were analyzed: fine-tuning the entire networks, fine-tuning the networks layer-wise, and, finally, freezing the weights of the entire network and applying SVM as a classification layer. The authors used a stochastic gradient descent for the optimizer, and the images were pre-classified as either DR or No DR to pose a binary classification problem. The accuracy measure used was the AUC of the ROC curve. The highest AUC achieved was obtained by fine-tuning the entire network, while the second-best performance was achieved by fine-tuning layer-wise. The VGG-S architecture obtained the highest AUC that was achieved for the Messidor dataset with an AUC of 98.34%. For the DR1 dataset, an AUC of 97.86% was obtained by using the same network. [PERSON] et al. [56] compared the InceptionV3 and Xception architectures to classify DR into two grades, DR or No DR, by using the Kaggle dataset. The authors used the whole dataset of 35126 images, with 20% of the images being used to test the algorithm's performance over unseen data. The authors fine-tuned the last two blocks of the two architectures and compared two optimizers with different LRs: stochastic gradient descent and Adam. The authors augmented the images by horizontally and vertically flipping the images or by shifting and rotating the images to increase the robustness of the model. The authors used the accuracy measure to assess the performance of the architectures. The reported results were 87.12% for the InceptionV3 architecture and 74.49% for Xception. [PERSON] et al. [57] trained a modified GoogLeNet architecture by using a private dataset. They used 9443 images to train the model and 496 to test it. They cropped the images to \(1272\times 1272\) pixels, and they considered a four-class classification scheme. The reported accuracy was 81%, and the kappa score was equal to 0.74. [PERSON] et al. [58] investigated the impact of transfer learning on the STARE dataset [52]. They used image augmentation techniques to increase the size of the dataset to 10,000 images, with ten retina disorder categories, including DR. The authors opted for the pre-trained VGG19 and AlexNet architectures.