## 3 Experiments
### Performance Testing on Other Datasets and Comparison with Previous Results  
We evaluated the proposed method with test datasets from DDR and Messidor-2. The models trained with both imbalanced and balanced EyePaCS training datasets were \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Dataset** & \multicolumn{3}{c}{**Imbalanced Training Set**} & \multicolumn{3}{c}{**Balanced Training Set**} \\ \hline  & **Metrics** & **ACC (\%)** & **TPR (\%)** & **TNR (\%)** & **ACC (\%)** & **TPR (\%)** & **TNR (\%)** \\ \hline Baseline & T(D) & 82.12 & 68.03 & 96.21 & 81.53 & 74.05 & 89.02 \\ \hline  & T(U)F(D) & 84.50 & 74.32 & 95.51 & 84.60 & 82.90 & 86.72 \\  & T(U)F(D)F(cls) & 85.81 & 81.84 & 90.29 & - & - & - \\ The proposed & T(D)L(U)\(\rightarrow\)D\({}^{\prime}\) T(D\({}^{\prime}\))F(D) & 84.99 & 78.52 & 90.38 & 86.40 & 82.80 & 90.90 \\ method & T(D)L(U)\(\rightarrow\)D\({}^{\prime}\) T(D\({}^{\prime}\))F(D) & 85.62 & 80.95 & 90.28 & - & - & - \\  & T(U)F(D)L(U)\(\rightarrow\)D\({}^{\prime}\) T(D\({}^{\prime}\))F(D) & - & - & - & 86.25 & 82.60 & 89.89 \\  & T(U)F(D)L(U)\(\rightarrow\)D\({}^{\prime}\) T(D\({}^{\prime}\))F(D) & 86.10 & 85.13 & 87.07 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of experiments performed with the proposed method (compared with the baseline). tested using these two additional datasets. Experimental results corresponding to the DDR and Messidor-2 test datasets are reported in Tables 5 and 6, respectively. The proposed method, which includes three techniques, (T(U)F(D), T(D)L(U)\(\rightarrow\)D\({}^{\prime}\) T(D\({}^{\prime}\))F(D), and T(U)F(D)L(U)\(\rightarrow\)D\({}^{\prime}\) T(D\({}^{\prime}\))F(D)), was tested on DDR and Messidor-2 datasets. We used the test results obtained from the three datasets, EyePaCS, DDR, and Messidor-2, to compare with other methods. Three studies of recent DR detection for comparison are those of [13; 14; 15]. All these previous studies used the supervised learning baseline. The authors used a customized CNN model [15], while they used models modified by adding other modules to state-of-the-art CNN models such as DenseNet, ResNet-50, and Inception to improve the performance [14; 15]. The test of model performance was conducted on EyePaCS and DDR, and accuracy (ACC) was used as one of the evaluation metrics [13]. Messidor-2 data was used to test the model, and the area under the curve (AUC), sensitivity (TPR), and specificity (TNR) were used as the evaluation metrics in [14]. Meanwhile, in [15], tests were performed using EyePaCS data, and the evaluation metrics were ACC, TPR, and TNR. The results that were reported in [13; 14; 15] are shown in Table 7. When using EyePaCS, [13] and [15] are compared with our proposed method; the best accuracies reported in [15] and [13] were 75% and 86.18%, respectively, which are lower than ours. The proposed method greatly outperforms the customized CNN model proposed in [15] in terms of TPR. CABNet [13], which addresses the imbalanced learning problem by adding a novel network called category attention block and a module called global attention block to the CNN model, outperformed their baselines and significantly improved performance. However, our wrapped approach of utilizing unlabeled data outperforms CABNet on the DDR test set substantially. In the case of Messidor-2 data, DLGP-DR, the authors of [14] added a Gaussian process to improve the generality of their model, and reported performance results of AUC 87.87%, TPR 72.37, and TNR 86.25, which are also significantly lower than ours.