# Diabetic Retinopathy Diagnosis Based on RA-EfficientNet
## 4 Conclusions  
In this paper, we focus on 2-grade classification and 5-grade classification, which can be used for doctors' diagnosis of DR, of which a 5-grade classification provides more accurate grading information. We conduct experimental studies on many current deep learning networks using transfer learning technology, including InceptionResNetV2, MobileNetV2, Xception, and EfficientNet, among which EfficientNet achieves the best results because of the balance of the depth, width and resolution, with a 97.95% and 91.00% accuracy for a 2-grade and 5-grade classification task, respectively. To achieve more efficient results, we adopted an RA block modeled on pre-trained CNN networks, and all the models with an RA block performed better than the original networks. After a small increase in the  
Figure 10: ROC Curves for severity grades of Diabetic Retinopathy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Precision (\%)** & **F1 (\%)** \\ \hline [PERSON] et al. [24] & 81.85 & 70.00 & 56.00 \\ [PERSON] et al. [15] & 82.00 & 69.00 & 66.00 \\ [PERSON] et al. [25] & 86.18 & NA & NA \\ RA-EfficientNet & **89.29** & **89.92** & **89.29** \\ \hline \hline \end{tabular}  
* NA: represents not applicable.  
\end{table}
Table 9: Comparison with existing works for DR severity grades on EyePACS dataset.  
parameters, the evaluation index of the two tasks reached 98.36% and 93.55% accuracy, respectively, using RA-EfficientNet, which shows that our RA block better distinguishes between the lesion features of DR images. The results demonstrate that our diagnostic model provides the best performance and a better evaluation value for the APTOS 2019 dataset, compared to existing DR classification methods. Meanwhile, we verify the robustness of the proposed RA-Efficientnet algorithm by training and evaluating an EyePACS dataset of fundus images, through which we achieved good performance.  
The advantages of this paper are as follows: (1) Two types of classification are realized in the model, proving its feasibility both for the diagnosis of DR and its severity levels; (2) Designs provided for the classification of DR, and the new network RA-EfficientNet performs better than other networks in comparison, with a higher accuracy and requires less computer-based calculations; (3) Combined with transfer learning, which can overcome the problem of a small sample size of DR, RA-EfficientNet has obtained satisfactory results. In the near future, we will further optimize the method to improve the accuracy of DR detection and try to develop a more powerful DR diagnosis model to assist doctors in clinical examinations.  
Conceptualization, S.-L.Y. and X.-L.Y.; methodology, X.-L.Y.; software, T.-W.W.; validation, F.-R.S., X.X. and J.-F.H.; formal analysis, S.-L.Y.; resources, X.-L.Y.; data curation, T.-W.W. and F.-R.S.; writing--original draft preparation, X.-L.Y.; writing--review and editing, S.-L.Y.; supervision, X.X.; project administration, J.-F.H.; funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 82060329); Yunnan Provincial Department of Education Project (No. 2020J0052).  
Not applicable.  
**Data Availability Statement**: Publicly available datasets were analyzed in this study. This data can be found here: [[[https://www.kaggle.com/c/diabetic-retinopathy-detection](https://www.kaggle.com/c/diabetic-retinopathy-detection)]([https://www.kaggle.com/c/diabetic-retinopathy-detection](https://www.kaggle.com/c/diabetic-retinopathy-detection))] accessed on 15 November 2021, [[[https://www.kaggle.com/c/aptos2019-blindness-detection/data](https://www.kaggle.com/c/aptos2019-blindness-detection/data)]([https://www.kaggle.com/c/aptos2019-blindness-detection/data](https://www.kaggle.com/c/aptos2019-blindness-detection/data))] accessed on 15 November 2021.  
The authors declare no conflict of interest.