## 2 Related Works  
By creating unique features and designing software that analyses retinal images, numerous techniques have been investigated to address the problem of DR grading. This kind of approach is complex and labor-intensive. The ophthalmologist must also use his knowledge of designing traits and labeling retinal pictures. On the other hand, this necessity is one of the significant issues that must be addressed [12]. Most of these traditional approaches are unsuccessful in figuring out the answers and understanding how things fit together. It retains many irrelevant picture-specific features, such as rotation, brightness, and size, which impact performance and accuracy rather than learning valuable data [13]. High-performance optic recognition and classification to identify and separate the afflicted retinal areas have been feasible with the advent of AI approaches such as machine learning (ML) and deep learning (DL) [14]. The basis of the transfer learning strategy theory is that the approach was previously trained on a sizable image database and could be adjusted to fit the necessary set of data [15]. This technique has become popular over the past few years because of the availability of large pre-trained models and the lack of labeled data for many real-world challenges [16]. The Quadratic Weighted Kappa was employed for analyzing the DR prediction in a study by [PERSON] [17]. They used EfficientNet b3 pre-trained model for the classification. In a paper published by [PERSON] [18], different models like VGG, Xception, ResNet, Inception, and EfficientNet were employed and achieved an accuracy of 99.36% with 0.986 recall. In [19], from the input dataset, features were extracted using a CNN model, and the classification was performed using a support vector machine model. Compared to classifying using the CNN model, this strategy offered faster execution. Medical imaging is crucial at all fundamental health difficulty levels and various medical diseases. DR grading from fundus photos has been the subject of much research using a variety of transfer learning approaches using publicly available datasets. [PERSON] et al., [20] employed Inception [21], VGG16 [22], DenseNet [23], MobileNet [24], and ResNet models [25], and VGG16 led to a 100% accuracy rate. The learnable parameters are reduced when multiple layers are stacked without using spatial pooling for CNN models, which are simpler to train [26]. [PERSON] and [PERSON] [27] constructed a support vector machine and Naive Bayes model with a binary pattern approach. The algorithm was applied to choose pertinent features. As opposed to this, the models were used to categorize patients as having the condition. Concerning accuracy, execution time, and memory use, the support vector machine performs better than the naive Bayes. [PERSON] et al. [28] sought to build an accessible predictor and classifier for hard exudates using artificial neural networks (ANN). Feature extraction and detection were performed using the Speed Up Robust Features technique. For classification, however, Feed-Forward Backpropagation ANN was employed. The work's major flaw is that it only used a dataset containing 48 photos. [PERSON] et al., compare multiple designs, including Inception-ResNetV2, ResNet50, NASNet, InceptionV3, VGG16, DenseNet121, Xception, and VGG19 in the article [29]. The models carried out classification and localization tasks, which were trained on a proprietary dataset and tested on the Messidor-2 dataset. The CNN and Contrast Limiting Adaptive Histogram Equalization (CLAHE) approaches increased the area under the curve and the fundus images' quality (23). The CLAHE technique has been introduced, amplifying the retinal vessels to enhance the preprocessing stage. Various ML classifiers have been merged to enhance DR detection and reduce the error rate [30]. Accurate diagnosis and DR measurement can be performed using fundus images. An ophthalmologist must have much training and dedication to analyze DR using fundus images manually.