## 4 Results
### Result Analysis
\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 Inception-ResNet-v2 & 0.8350 & 0.5976 & 0.5243 & 0.3367 \\ ViT Base & 0.8447 & 0.6812 & 0.4757 & 0.2867 \\ Inception-ResNet-v2 + ViT Base & 0.9515 & 0.8970 & 0.9126 & 0.8809 \\ ResNet152V2 + ViT Base & 0.9515 & 0.8913 & 0.9126 & 0.8838 \\ DenseNet121 + ViT Base & 0.9519 & 0.8664 & 0.8046 & 0.6814 \\ MobileNetV3 Large + ViT Large & 0.9515 & 0.8838 & 0.6990 & 0.5943 \\ ResNet152V2 + ViT Large & 0.9515 & 0.8970 & 0.9029 & 0.8721 \\ ResNet152V2 + Swin Transformer & 0.9903 & 0.9873 & 0.9601 & 0.9301 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different teacher models on the IDRiD dataset. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Size (MB)** & **Total Parameters** & **Trainable Parameters** \\ \hline NASNetMobile & 19.4 & 4.5 M & 0.2 M \\ DenseNet201 & 73.7 & 18.8 M & 0.4 M \\ MobileNetV2 & 10.4 & 2.6 M & 0.3 M \\ MobileNet & 13.6 & 3.5 M & 0.2 M \\ ResNet50 & 92.5 & 24.2 M & 0.5 M \\ EfficientNetV2B0 & 24.6 & 6.3 M & 0.3 M \\ EfficientNetB7 & 248.7 & 64.7 M & 0.6 M \\ EfficientNetB4 & 70.2 & 18.2 M & 0.4 M \\ Xception & 82 & 21.4 M & 0.5 M \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Student model information and parameters. \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 Inception-ResNet-v2 & 0.9618 & 0.9236 & 0.7844 & 0.6601 \\ ViT Base & 0.9127 & 0.8250 & 0.7190 & 0.5666 \\ Inception-ResNet-v2 + ViT Base & 0.9591 & 0.9180 & 0.8158 & 0.7207 \\ ResNet152V2 + ViT Base & 0.9741 & 0.9484 & 0.8786 & 0.8199 \\ DenseNet121 + ViT Base & 0.9673 & 0.9352 & 0.8240 & 0.7387 \\ MobileNetV3 Large + ViT Large & 0.9482 & 0.8983 & 0.7763 & 0.9834 \\ ResNet152V2 + ViT Large & 0.9891 & 0.9781 & 0.9468 & 0.9186 \\ ResNet152V2 + Swin Transformer & 0.9932 & 0.9864 & 0.9891 & 0.9061 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with different teacher methods on the APTOS dataset. Figure 11: Multi-class accuracy of different student models on the APTOS and IDRiD datasets. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Multi-Class Accuracy** & **Multi-Class MCC** & **Multi-Class Accuracy** & **Multi-Class MCC** \\  & **(APTOS)** & **(APTOS)** & **(IDRiD)** & **(IDRiD)** \\ \hline NASNetMobile & 0.8158 & 0.7134 & 0.7723 & 0.6363 \\ DenseNet201 & 0.8322 & 0.7523 & 0.8208 & 0.7190 \\ MobileNetV2 & 0.8022 & 0.6911 & 0.7903 & 0.5047 \\ MobileNet & 0.8336 & 0.7450 & 0.7894 & 0.6809 \\ ResNet50 & 0.7531 & 0.6152 & 0.6239 & 0.4216 \\ EfficientNetV2B0 & 0.4748 & 0.0000 & 0.5170 & 0.4003 \\ EfficientNetB7 & 0.4911 & 0.0000 & 0.4658 & 0.3583 \\ EfficientNetB4 & 0.6112 & 0.3917 & 0.5062 & 0.6419 \\ Xception & 0.8623 & 0.8194 & 0.9041 & 0.8850 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of base models on the APTOS and IDRiD datasets. \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 & 0.9591 & 0.9180 & 0.8022 & 0.7060 \\ DenseNet201 & 0.9673 & 0.9346 & 0.8472 & 0.7738 \\ MobileNetV2 & 0.9645 & 0.9298 & 0.8308 & 0.7408 \\ MobileNet & 0.9714 & 0.9429 & 0.8527 & 0.7747 \\ ResNet50 & 0.9372 & 0.8748 & 0.7135 & 0.5720 \\ EfficientNetV2B0 & 0.6985 & 0.4643 & 0.5157 & 0.0000 \\ EfficientNetB7 & 0.8226 & 0.6510 & 0.5102 & 0.0000 \\ EfficientNetB4 & 0.8663 & 0.7312 & 0.6371 & 0.4530 \\ Xception & 0.9673 & 0.9345 & 0.8799 & 0.8194 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of different student models on the APTOS dataset. Since Xception performed the best among the nine classifiers, it was further modified by integrating a modified CBAM block.