# Diabetic Retinopathy Diagnosis Based on RA-EfficientNet
## 3 Experiments
### Experimental Results and Discussion
#### 3.2.2 Task 2: 5-Grade Classification  
Task 2 is a series of experiments for a 5-grade classification, which can make doctors' treatment more convenient and improve them by identifying the severity of DR. The APTOS 2019 dataset already has five grades, which is required for the task. The proportion in the training set and the testing set is the same as for task 1. Based on the 5-grades dataset, our diagnostic model and comparison models are conducted, and the results are presented below. Table 6 lists the comparison of the same networks as task 1 on severity grades. The results show that EfficientNet obtains the highest values compared with other networks in accuracy, precision, F1 and kappa score, for which the value are 91.00%, 91.03%, 91.01%, and 88.74%, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **F1 (\%)** & **Kappa (\%)** \\ \hline InceptionResNetV2 & 85.55 & 85.65 & 85.56 & 81.93 \\ Xception & 90.80 & 90.73 & 90.75 & 88.49 \\ MobileNetV2 & 90.55 & 90.70 & 90.57 & 88.18 \\ EfficientNet & **91.00** & **91.03** & **91.01** & **88.74** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different CNN networks for Task 2. Figure 8: Confusion matrix of EfficientNet and RA-EfficientNet for task 1. Table 7 is the comparison of different CNN networks with RA block for task 2. From this table, the same conclusion as in Table 3 can be drawn. Figure 9 is the confusion matrix of EfficientNet and RA-EfficientNet for task 2. From Figure 9, we can see that the total number classified as No-DR, mild, moderate, severe and proliferative DR using EfficientNet, is 402, 350, 317, 395, and 356, respectively. For the moderate class, the rate of correct classification is lower than that in other severity levels, because the features of retinal images are difficult to identify at this severity level. From Figure 9, we can see that the addition of RA significantly improves the classification results in the moderate level, and strengthens the accuracy for other levels. The ROC curve of our RA-EfficientNet model is shown in Figure 10. The ROC curve and AUC value represent how close the prediction is to a perfect classification, which is presented at the top left corner of the ROC coordinate. The AUC value shows the area under the ROC curve. The closer the value is to 1, the better the model performs. The figure shows the AUC value of 100%, 96.00%, 97.00% and 95.00%, representing No-DR, mild, severe and proliferative DR classes, respectively. Since a morphological variation of the fundus images, for the moderate grades, affects the recognition of pathological structures, the AUC of moderate DR is lower than the others with 92.00%. Table 8 presents the comparison between our 5-grade diagnostic model and the five existing models proposed in Section 1, all of which are based on APTOS 2019 dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **F1 (\%)** & **Kappa (\%)** \\ \hline InceptionResNetV2 + RA & 91.55 & 91.66 & 91.57 & 89.43 \\ Xception + RA & 93.00 & 93.06 & 93.02 & 91.24 \\ MobileNetV2 + RA & 92.60 & 92.75 & 92.65 & 90.74 \\ RA-EfficientNet & **93.55** & **93.62** & **93.57** & **91.93** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of different CNN networks and RA block for Task 2. Figure 9: Confusion matrix of EfficientNet and RA-EfficientNet for task 2. From Table 8, we can see that the RA-EfficientNet, used in this paper, achieves the highest performance accuracy for DR severity grades predictions. The model's accuracy improves by 2.55% as compared with the most advanced model recently proposed by [PERSON] et al. [16]. To further prove the robustness of our RA-EfficientNet, we train and test the proposed RA-EfficientNet on the EyePACS dataset [23], from which we selected a total of 11,756 images. Table 9 shows a comparison of our RA-EfficientNet model, including three recent studies by [PERSON] et al. [24], [PERSON] et al. [15] and [PERSON] et al.