## 4 Results
### Result Analysis
Although the integration of the CBAM module increased its size by 20 megabytes, it ameliorated its binary class accuracy by 3.27% and multi-class accuracy by 11.04% on the APTOS dataset. Moreover, there was an enhancement of 1.82% in binary class accuracy and 1.38% in multi-class accuracy on the IDRiD dataset. This significant performance improvement can be attributed to the modified CBAM block's improved adaptability, which improves feature prioritization and selection. Given that the original Xception model, while powerful, may include extraneous features from its pre-training on ImageNet, the CBAM module effectively focuses on the salient features that are specifically pertinent to diabetic retinopathy. This feature improvement leads to a significant increase in classification accuracy, demonstrating the effectiveness of the CBAM module in enhancing the model's representational capabilities. On the APTOS dataset, the proposed student model thereby achieves 0.99045, 0.9907, 0.9905, 0.9903, and 0.9856 in the accuracy, precision, recall, F1 score, and MCC, respectively, on multi-class classification, and a full 1.0 in all metrics for the binary class classification. The confusion matrix, presented in Figure 12, shows that only four moderate images are misclassified as mild and severe. Other than moderate, no other types have more than two misclassifications. Moreover, the confusion matrix emphasizes that the impact of class imbalance on the results is insignificant, as there are no extra predictions in the majority class (no DR). The ROC curve represents the model's ability to discriminate true positive instances from false positive instances. The proposed model's ROC curve, presented in Figure 13, shows a perfect area under the curve with the highest value of 1.0. These results illustrate the model's discriminate ability in classifying DR images. Similarly, on the IDRiD dataset, it achieves 0.9805, 0.9811, 0.9805, 0.9804, and 0.9576 in accuracy, precision, recall, F1 score, and MCC, respectively, on multi-class classification, and 0.9417, 0.9429, 0.9417, 0.9415, and 0.9217 in accuracy, precision, recall, F1 score, and MCC, respectively, on binary classification. The confusion matrix, presented in Figure 14, shows that--similar to the APTOS dataset--the model also faces some challenges in classifying two mild-type DR images in the IDRiD dataset. Other than that, one moderate image and one severe DR image are misclassified as mild and proliferate DR, respectively. The ROC curve presented in Figure 15 also certifies its discriminative ability in differentiating true positive instances from false positive ones with an average ROC of 0.99. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Binary Class Accuracy** & **Binary Class MCC** & **Multi-Class Accuracy** & **Multi-Class MCC** \\ \hline NASNetMobile & 0.9130 & 0.7980 & 0.7819 & 0.6696 \\ DenseNet201 & 0.9252 & 0.6341 & 0.8304 & 0.7507 \\ MobileNetV2 & 0.9530 & 0.8490 & 0.8941 & 0.8290 \\ MobileNet & 0.9158 & 0.4480 & 0.8836 & 0.6973 \\ ResNet50 & 0.8224 & 0.6020 & 0.8579 & 0.6138 \\ EfficientNetV2B0 & 0.7757 & 0.4583 & 0.5970 & 0.4801 \\ EfficientNetB7 & 0.7280 & 0.4561 & 0.5412 & 0.4397 \\ EfficientNetB4 & 0.7273 & 0.1267 & 0.5170 & 0.4003 \\ Xception & 0.9623 & 0.9094 & 0.9279 & 0.892 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of different student models on the IDRiD dataset. Figure 12: Concision matrix of the student model on the APTOS dataset. Figure 13: ROC curve of the student model on the APTOS dataset.