## 2 Related Works
### Deep Learning-Based Approach  
Deep learning, especially convolutional neural networks (CNNs), is gaining widespread popularity and finding increased application in various domains, notably in medical image processing. This approach, distinct from traditional feature engineering techniques described in Section 2.1, addresses challenges such as medical image segmentation, detection, and classification, achieving substantial success. To diagnose various ocular diseases from fundus images, different algorithms based on deep learning have been developed, generally showing high performance [23; 24; 25; 26; 27]. The deep learning-based approach lies in its ability to automatically learn feature extraction through a backbone network, primarily composed of convolutional and pooling layers. An antecedent study employing deep learning in DME grading is found in [12], where the authors presented a deep neural network comprising 13 convolutional layers for feature extraction and two dense layers dedicated to DME grading. The model demonstrated noteworthy performance, achieving accuracy, sensitivity, and specificity values of 88.8%, 74.7%, and 96.5%, respectively.  
Some other authors demonstrated that ensemble methods perform well for DME grading, as in the work outlined in [18], where the authors advocated a two-stage approach. In the initial stage, the algorithm discerns the presence or absence of DME. Upon confirming the presence of DME, the image is then subjected to the second stage, where grading is conducted based on the severity of the condition. Both stages integrate the proposed technique, termed the Hierarchical Ensemble of Convolutional Neural Networks (HE-CNN). The results demonstrated notable accuracy, sensitivity, and specificity values of 95.47%, 94.68%, and 97.19%, respectively.  
In due course, natural language processing algorithms found applications in computer vision, leveraging their notable performance in natural language applications. Consequently, certain researchers began implementing these algorithms for DME grading. A notable example is the research documented in [5], where the authors introduced an innovative end-to-end architecture incorporating ResNet50 along with channel attention (SENet) for feature extraction. Moreover, they introduced a disease attention module to enhance disease-specific information related to DME. The outcomes demonstrated an accuracy value of 97.06%. In other research presented in [20], the authors introduced a three-component system encompassing the backbone, the neck, and the head. The Swin Transformer served as the framework's backbone, incorporating window multi-head self-attention (W-MSA) and mobile window multi-head self-attention (SW-MSA). The neck of the framework was composed of Global Average Pooling (GAP), while the head utilized linear CLS (class token). This modular design contributes to the model's simplicity and ease of training, as it ensures a clear mapping between features and categories. The results were 98.66%, 98.66%, 99.32%, and 98.66% for accuracy, sensitivity, specificity, and F1-score, respectively. It is worth mentioning that the above four methods use the public Messidor data set to grade the three severity degrees of DME. Other methods [13; 15; 16; 19] use private data sets or their classification is binary (presence or absence of DME).