# Classification of Diabetic Retinopathy Based on Efficient Computational Modeling
###### Abstract  
Convolutional neural networks (CNN) and Vision Transformers (ViT) have long been the main backbone networks for visual classification in the field of deep learning. Although ViT has recently received more attention than CNN due to its excellent fitting ability, their scalability is largely limited by the quadratic complexity of attention computation. For the determination of diabetic retinopathy, the fundus lesions as well as the width, angle, and branching pattern of retinal blood vessels are characterized, inspired by the ability of Mamba and VMamba to efficiently model long sequences, VMamba-m is proposed in this paper. This is a generalized visual skeleton model designed to reduce computational complexity to linear while retaining the advantageous features of ViTs. By modifying the cross-entropy loss function, we enhance the model's attention to rare categories, especially in large-scale multi-category classification tasks. In order to enhance the adaptability of the VMamba-m model in processing visual data, we introduce the se channel attention mechanism, which enables the model to learn features in the channel dimension and form the importance of each channel. Finally, different weights are assigned to each channel through the incentive part. In addition to this, this paper further improves the implementation details and architectural design by introducing a novel attention mechanism implemented based on the local windowing method, which aims to optimize the model's ability in processing long sequence data to enhance the performance of VMamba-m and improve its inference speed. Extensive experimental results show that VMamba-m performs well in the retinopathy V classification task, and it has significant advantages in terms of accuracy and computation time over existing benchmark models. classification of diabetic retinopathy; deep learning; attention mechanism; diabetic retinopathy 2024 14 11327 11327 11327 12024 14 11327  
improved, including multimodal capabilities, performance improvements, and security considerations. It provides a comprehensive perspective for understanding the capabilities and challenges of modern large-scale language models. By showing how to use large-scale models for efficient learning, researchers have laid the foundation for smarter artificial intelligence systems. To represent complex patterns in visual data, two major classes of backbone networks, namely convolutional neural networks (CNN) [7; 8; 9; 10; 11] and visual transformers (ViTs) [12; 13; 14; 15], have been proposed and are widely used for various visual tasks. These articles proposed a variety of deep convolutional neural network architectures, provided pre-trained models and other contributions, which greatly promoted the development of deep learning in the field of computer vision and became an important cornerstone in this field. It further promoted the application of self-attention mechanism in visual tasks, proved the potential of the Transformer model in image processing, and inspired other subsequent research work. By proposing a hierarchical design and moving window mechanism, the efficiency and performance of Transformer in computer vision tasks are significantly improved. These innovations not only make Transformer a powerful visual model, but also provide new perspectives and ideas for subsequent research, promoting the development of the field of computer vision. Compared to CNNs, ViTs often exhibit superior modeling capabilities and generally incorporate a self-attention mechanism [14; 1], which implements a global receptive field and dynamically predicted weighting parameters [16]. However, the addition of self-attention mechanisms to some models also has some side effects, such as a quadratic growth of the complexity of the self-attention mechanism with the increase in the input size, which leads to significant computational overhead in downstream tasks with large spatial resolution [17].