# Classification of Diabetic Retinopathy Based on Efficient Computational Modeling
## 5 Conclusions  
This paper presents the improved model VMamba-m, designed for efficient visual representation learning using state space models (SSM). The advantages of selective SSM, including global receptive fields, input-dependent weighting parameters, and linear computational complexity, are incorporated into visual data processing. In addition, to address the problem of poor classification accuracy of the existing methods, combined with the characteristics of tiny and diverse lesions in DR fundus images, this paper improves the original architectural design of the VMamba model by adding a two-branch attention mechanism to extract local features, which significantly improves its inference speed and accuracy. The VMamba-m and the original Mamba series models mentioned in this paper have been proved to be effective by a large number of experimental studies, exceeding the performance of the traditional models in the case of pre-processed datasets. In addition, VMamba-m exhibits significant scalability as the input resolution increases, showing minimal performance degradation while maintaining linear computational complexity. Due to the experimental environment and time constraints, this paper was not able to  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **Precision** & **AUC** & **F1** & **Time** \\ \hline VMamba & 0.714 & 0.500 & 0.718 & 0.871 & 0.500 & 76.322 \\ \hline VMamba + Recall & 0.722 & 0.504 & 0.724 & 0.874 & 0.510 & 75.122 \\ \hline VMamba + SE & 0.742 & 0.507 & 0.723 & 0.878 & 0.501 & 36.081 \\ \hline VMamba + Local & 0.746 & 0.502 & 0.734 & 0.879 & 0.500 & 36.255 \\ \hline VMamba + Focal + SE & 0.771 & 0.517 & 0.733 & 0.881 & 0.506 & 34.391 \\ \hline VMamba + Focal + Local & 0.766 & 0.514 & 0.742 & 0.886 & 0.515 & 35.295 \\ \hline VMamba-m & 0.791 & 0.537 & 0.766 & 0.891 & 0.527 & 34.027 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Five-category ablation experiments on the APTOS2019 dataset.  
potentially explore on more directions. The future research direction of this paper can still be expanded in the following aspects: (1) Although the Mamba series model is faster and more accurate than the traditional model in each round of iteration, there is still room for development in terms of precision, recall, and F1 score compared to the traditional model. (2) The Mamba series model can be applied to image processing in more medical fields, which greatly improves the accuracy and efficiency of medical image analysis, and provides more accurate data support for the diagnosis and treatment of medical imaging.  
Conceptualization, Q.D.; methodology, Q.D. and J.W.; software, Y.B. and S.Z.; validation, J.X. and Y.B.; writing--original draft preparation, J.W.; writing--review and editing, J.X., Q.D. and J.W.; visualization, Q.D. and Y.B.; funding acquisition, Q.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Development Plan Project of the Jilin Provincial Science and Technology Department (YDZJ202201 ZYTS569), funder: Jilin Provincial Department of Science and Technology; Project of Jilin Educational Science Planning (No. GH20245), funder: Office of Jilin Provincial Education Science Research Leading Group.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were used in this study. These data can be found here: [[https://www.kaggle.com/competitions/aptos2019-blindness-detection/data](https://www.kaggle.com/competitions/aptos2019-blindness-detection/data)]([https://www.kaggle.com/competitions/aptos2019-blindness-detection/data](https://www.kaggle.com/competitions/aptos2019-blindness-detection/data)) (accessed on 28 June 2019).  
We would like to express our deepest gratitude to all those who have contributed to the completion of this research and the writing of this paper.  
The authors declare no conflicts of interest.