## 1 Introduction
CNNs can process two-dimensional (2D) data such as images and has good spatial feature extraction capability [22]. CNN models such as Inception V3, Xception, and ResNet 50 can automatically diagnose and stage DR [23]. In addition, Vision Transformer (ViT) [24]-based image processing application attracts much attention due to a self-attention mechanism to concentrate on the global dependence of image patches and achieves superior classification performance. Although the deep learning-based medical image processing method achieves superior achievements, the DR fundus images collected by WSN possess three characteristics:  
Figure 1: Five clinical grades of DR depending on the fundus features: (**a**) no DR, (**b**) mild DR, (**c**) moderate DR, (**d**) severe DR, and (**e**) PDR. irregular region distribution, blurred pixels and low contrast, and image diversity, which challenge the performance of deep learning. 1. Irregular region distribution. The regional distribution of DR is irregular, and there is some individual variability in the vascular pathways of the fundus. Fundus lesions and complete fundus information are required to rate DR [25]. Further, the deep learning model's ability to capture global lesion locations is equally essential. CNN focuses only on the presence or absence of the target to be detected and not on the location and relative spatial relationships between components. [PERSON] et al. argued that the local receptive wild operations, such as convolution operations, limit the acquisition of long-range pixel relationships [26]. [PERSON] et al. concluded that the CNN algorithm ignored some valuable DR-related lesions [27]. The Transformer, a neural network architecture that relies on a self-attentional mechanism to process sequential data, has a large effective acceptance area and can learn global representation [28; 29]. However, since it requires a large amount of training data, the time required for training is relatively long. 2. Blurred pixels and low contrast. The lesion area may have blurred pixels and low contrast with the surrounding area [30; 31]. Lesion pixels that are too close to pixels in other areas will show up as blurred pathological features in fundus images. In image classification tasks, \"fuzzy pixels\" refer to the situation where an image pixel has an unclear or ambiguous boundary, making it difficult for the model to classify the pixel's content accurately. \"Low contrast\" refers to images with a narrow range of brightness or colour values, resulting in reduced visual contrast between objects and making it difficult for the model to distinguish between them. The dataset in their experiment had images with various distortion types, including low-contrast and fuzzy pixels. These could negatively impact the performance of the classification model. The contrast between light and dark is not apparent (Figure 1e), which means the image has low contrast and looks blurry. 3. Image diversity. The data diversity of fundus images is mainly affected by the following factors: patient demographics, imaging equipment, and image acquisition settings. The age, race, gender, medical history, and other factors of patients will affect the appearance of fundus images [32]. For example, elderly patients may show more signs of age-related macular degeneration, while diabetes patients may show characteristic symptoms of diabetic retinopathy. The imaging device's type, brand, model, and parameter settings also affect the quality and resolution of fundus images [33]. Image acquisition settings, such as lighting, contrast, and magnification, and image preprocessing steps, such as image enhancement, filtering, and normalization, also affect the appearance and characteristics of images. An unbalanced number of images under each category for a dataset, including multiple sample categories, will also affect the model training effect. In addition, there are different conditions under which fundus images are taken, as demonstrated by differences in the lighting, shooting angles, and varying degrees of noise.