## 5 Paper Review
The authors used a stochastic gradient descent (SGD) as an optimizer with an LR of 0.1 for training from scratch and an LR of 0.01 for fine-tuning the network. The authors reported that transfer learning increased the accuracy of the model used. [PERSON] et al. [65] investigated the usage of the InceptionV3 architecture to detect DR in the Kaggle dataset. The authors chose 2500 images and cropped them to \(300\times 300\) to train the model, and 5000 images were used to test the model. The authors pre-classified the images as either DR or No DR to make it a binary classification task. They employed a stochastic gradient descent as an optimizer, with an LR of 0.0005, to fine-tune the neural network. The reported result was a 90.9% accuracy and a 3.94% loss. [PERSON] et al. [66] considered the pre-trained InceptionV3 architecture to classify DR on 7023 images of the Kaggle dataset. The authors adopted a five-stage classification approach with the quadratic weighted kappa as an accuracy measure. The images were cropped to \(229\times 229\), and a stochastic gradient descent was used as an optimizer. Image augmentation was used with an early stop for 15 iterations to overcome the overfitting of the network. The reported Kappa score was 0.64, with an accuracy of 80%. [PERSON] et al. [67] proposed a novel Siamese-like architecture in which left and right fundus images were classified together. Siamese neural networks are networks with two parallel neural networks, and each of these networks takes different inputs. The authors used the Kaggle dataset with 28,104 training images split between right and left eyes and 7024 to test the architecture. They used the pre-trained InceptionV3 network on the ImageNet dataset. The authors examined the five-stage classification, as proposed by [PERSON] et al. [10], and opted to use a binary class classification. They used Adam as an optimizer, quadratic weighted kappa as the accuracy measure for the multiclass classification, and the AUC of the ROC for the binary classification. All the layers of InceptionV3 were fine-tuned, and the images were cropped to \(229\times 229\). The authors augmented images by randomly flipping them horizontally and by randomly applying a geometric transformation to increase the dataset's size and to control overfitting. They normalized all images from [0,255] to [-1,+1]. They reported the kappa result as 0.829 for the multiclass classification and an AUC of 95.1% for the binary classification. [PERSON] et al. [68] used a private dataset with 13,767 images to propose a model called DeepDR, which uses deep learning based on transfer learning models to detect DR. The model consists of three stages: identification, grading, and reporting. The identification stage is a binary classification model to predict the presence of DR. If DR exists, then the image is graded by using the grading stage of the four stages of DR; the last stage reports the result of the model. The authors used InceptionV3, Xception, and InceptionResNetV2 for feature extraction in the identification system. Moreover, they added a global average pooling layer to normalize the output of the feature extractor, and they subsequently added four dense layers with sizes 1024, 512, 256, and 128, respectively. A dropout layer between the dense layers, with a probability of 50%, was employed to limit overfitting. Due to its speed of convergence, the authors opted for the LeakyReLU activation function with a \(\beta\) of 0.2 and, in the end, a softmax layer to sum up the probabilities to 100%. For the grading system, the authors used ResNet50, DenseNet169, and DenseNet201 for feature extraction in the grading system. They then added a global average pooling layer and four dense layers with sizes 2048, 1024, 512, and 256, respectively. They employed a dropout layer between the dense layers with a probability of 50%, LeakyReLU, as the activation function for all the dense layers with \(\beta\) of 0.2 and, in the end, the softmax layer.