## 5 Paper Review
An ensemble was created to increase the network accuracy, and K-fold validation with k = 5 was used to validate the results. The highest accuracy that was obtained by the authors was achieved by using VGG19 architecture with random forest (RF) as a classifier. [PERSON] et al. [59] investigated transfer learning techniques by using three network architectures: AlexNet, VGG16, and InceptionNetV3. The authors used 166 images from the Kaggle dataset to tune the algorithms. The authors opted for the five-stage classification approach instead of the binary classification approach that has been used by other authors for this specific dataset. Additionally, they employed a stochastic gradient descent optimizer with Nesterov momentum to accelerate the convergence to the minimum. The authors cropped the images for each architecture to \(227\times 227\) for AlexNet, \(224\times 224\) for VGG16, and \(299\times 299\) for InceptionV3. They used the accuracy of the network as the evaluation metric, and they used \(K-fold\)_validation_ with \(K=5\) to cross-validate the results. The best-reported accuracy was 63.2% for the InceptionV3 architecture. [PERSON] et al. [60]used 580 images from the Messidor dataset to test the transfer learning of AlexNet. They opted for a two-class classification, and they cropped the images to \(227\times 227\). They achieved an 88.3% accuracy on the test set, which consisted of 290 images. [PERSON] et al. [61] considered the sliding windows algorithm, where small patches from the original images are used to train the CNN. These patches contain the important features of each image, such as the presence of exudates or microaneurysms. The authors used the Kaggle dataset to extract these patches. They extracted 1324 patches from 243 images and split these patches into training and testing datasets. They tested the proposed algorithm by using the E-Optha dataset, which contained 195 images. They used GoogLeNet architecture to train the model with an input size of \(128\times 128\). The authors considered a multi-class classification task with five DR grades. They resized the test images to \(2048\times 2048\) and normalized the pixels to test the model. Subsequently, the trained model crossed over the test image to produce a heat map with a probability score for every one of the five grades. The authors compared five pre-trained architectures (AlexNet, VGG16, GoogLeNet, ResNet, and InceptionV3) for binary classification and multi-class classification. The best performing architecture was InceptionV3 with a multi-class accuracy of 96% and a binary-class accuracy of 98%. [PERSON] et al. [62] trained a CNN by using transfer learning of the AlexNet, VGG16, and GoogLeNet models, and they utilized Kaggle two-class output. The authors reported that GoogLeNet achieved the highest sensitivity of 95% and specificity of 96%. The authors tried to utilize the multi-class Kaggle dataset, but they stated that a CNN cannot learn mild class sensitivity. The authors achieved decent results for detecting mild grades when using the Messidor dataset. [PERSON] et al. [63] compared the difference between transfer learning and learning from scratch. The authors used four CNN architectures, namely AlexNet, ResNet, GoogLeNet, and VGG. The authors performed their experiments on the full Kaggle dataset, and they used the AUC of the ROC curve, accuracy, sensitivity, and specificity as evaluation criteria. The authors reported that transfer learning did significantly increase the performance of CNN, with VGG-S producing the highest AUC. [PERSON] et al. [64] studied the difference between the performance of DenseNet with and without fine-tuning. The authors examined their method on a private dataset with 10,000 images and five grades. The authors used image augmentation to increase the size of the dataset and to balance the dataset between different classes. The final dataset contained 20,000 images that were distributed equivalently between the five classes.