# Classification of Diabetic Retinopathy Based on Efficient Computational Modeling
###### Abstract
To address this problem, considerable efforts have been made to improve the efficiency of attention, mainly by imposing constraints on the size of the computational window or the step size [18; 15; 19]. For example, the main contribution of ConvBERT [20] is the introduction of a dynamic convolution mechanism to enhance the local context understanding ability of the BERT model. By combining convolutional layers, ConvBERT achieves significant performance improvements in multiple natural language processing tasks, demonstrating the potential of convolutional neural networks in processing text data. Lite Transformer's [21] new model aims to improve the computational efficiency and performance of Transformer by optimizing the attention mechanism. By introducing the long- and short-range attention mechanism, the computational efficiency and performance of the model are significantly improved. Lite Transformer provides new solutions for efficient natural language processing in resource-limited environments and opens up new directions for future model design. [PERSON], [PERSON] et al. [22] proposed a new model architecture based on dynamic convolution, aiming to improve computational efficiency by reducing reliance on global self-attention. This model architecture not only demonstrates the potential of lightweight convolution in natural language processing, but also provides important ideas for designing more efficient deep learning models. Linformer [23] significantly reduces computing and memory overhead through low-rank projection technology, making it more efficient when processing long sequences. It has demonstrated excellent performance in multiple natural language processing tasks and promoted the further development of self-attention mechanisms. Provides efficient solutions to the computational challenges in long sequence processing. The main contribution of Longformer [24] is to propose a new Transformer architecture, which is specially optimized for long document processing. By introducing a sparse self-attention mechanism, Longformer significantly improves the efficiency of processing long texts and reduces computational complexity while maintaining good performance. This research provides an effective solution for natural language processing of long documents and provides new ideas for future model design. [PERSON]'s [25] main contribution is to propose a new Transformer architecture, specifically optimized for long sequence data. By introducing a sparse attention mechanism, Big Bird significantly reduces computational complexity and enhances the ability to model long-distance dependencies, enabling it to effectively handle long documents and complex natural language processing tasks. Although these methods are effective, they inevitably create a trade-off between effective receptive field and computational efficiency, thus limiting the ability to establish long-range dependencies in visual data. In fact, the position of Transformer in the field of large models can be said to be difficult to shake. However, the limitations of this dominant architecture for large models have become more and more obvious as the scale of the model expands and the sequences that need to be processed become longer. The emergence of the Mamba family of models is changing all of this in a powerful way. Its excellent performance is quickly gaining widespread attention. The Vision Mamba (Vim) proposal has already demonstrated its great potential to become the next-generation backbone of vision-based models. Researchers from the Chinese Academy of Sciences, Huawei, and Pengcheng Labs have proposed VMamba: a vision Mamba model with global receptive field and linear complexity [26]. Diabetic retinopathy (DR), as a complication of diabetes mellitus, is caused by lesions in the microvessels of the fundus, which in turn lead to retinal hemorrhage, edema, ischemia, retinal proliferative membrane formation, and retinal detachment, and ultimately lead to blindness in patients.