# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 5 Results
### Private Dataset

Table 5: Summarized glaucoma classification result of AIHUB. Table 7 presents the comparative analysis with previous studies. Many studies, as mentioned earlier, only mentioned AUC without providing a confusion matrix. Additionally, due to the issue of data scarcity, many studies combined multiple datasets for analysis, making it difficult to directly compare with our study. Nevertheless, the comparison of the results is as follows:  
The results of the top four teams in the REFUGE competition ranged from 0.9885 (VRT team) to 0.856 AUC ([PERSON] et al. [36]), where [PERSON] et al. [36] achieved results similar to the best ones. For the ORIGA dataset, compared to the BAJWA study, we \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & **Confusion Matrix** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** \\ \hline COLORMAP (JET) & 26 & 3 & 0.8983 & 0.8966 & 0.9000 & 0.8966 \\ \hline COLORMAP (MAKO) & 26 & 3 & 0.8136 & 0.7647 & 0.8800 & 0.8966 \\ \hline Ensemble & 27 & 2 & 0.9322 & 0.9310 & 0.9333 & 0.9310 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Method** & **Database** & **Performance** \\ \hline VRT [33] & custom CNN & REFUGE & AUC: 0.9885 \\ \hline SDSAIRC [33] & ResNet-50 & REFUGE & AUC: 0.9817 \\ \hline CURMMED [33] & - & REFUGE & AUC: 0.9644 \\ \hline NKSG [33] & SENet & REFUGE & AUC: 0.9587 \\ \hline Mammoth [33] & ResNet18, CatGAN & REFUGE & AUC: 0.9585 \\ \hline  & DenseNet201, ResNet18 & REFUGE & Specificity: 0.75 \\ [PERSON] et al. [36] & ResNet18 & DRSHTI-GS1 & AUC: 0.886, Sensitivity: 0.85 \\ \hline [PERSON] et al. [37] & Pretrained CNN & REFUGE & ACC: 0.9559 \\  & SVM & REFUGE & AUC: 0.9510 \\ \hline [PERSON] et al. [38] & 6 layers CNN & ORIGA, SCES & AUC: 0.831 \\ \hline [PERSON] et al. [13] & VGG CNN & ORIGA & AUC: 0.8384 \\ \hline [PERSON] et al. [39] & CNN & ORIGA, SCES & AUC: 0.822 \\ \hline [PERSON] et al. [40] & CNN & ORIGA & ACC: 0.9672 \\ \hline [PERSON] et al. [41] & CNN & HRF, ORIGA, DRSHTI-GS1 & ACC: 0.936 \\ \hline [PERSON] et al. [42] & Custom CNN & ACRIMA, ORIGA, RIM-ONE & ACC: 99.5 \\ \hline [PERSON] et al. [19] & Ensemble ResNet & RIM-ONE, ORIGA, DRSHTI-GS1 & ACC: 0.91 \\ \hline [PERSON] et al. [43] & CNN & ORIGA & ACC: 0.55 \\ \hline [PERSON] et al. [44] & ResNet50 & G1020 & ACC: 0.9848 \\  & & ORIGA & ACC: 0.97 \\ \hline  & & ORIGA & ACC: 0.9259, Sensitivity: 0.9839 \\ \hline Proposed & EfficientNetV2 & REFUGE & ACC: 0.9875 \\  & & & AUC: 0.9880 \\  & & & Sensitivity: 0.9 \\ \hline  & & & & ACC: 0.8308 \\  & & & AUC: 0.8452 \\  & & & Sensitivity: 0.7647 \\ \hline  & & & & ACC: 0.9853 \\  & & & AUC: 0.9846 \\  & & & Sensitivity: 0.9831 \\ \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with the existing state-of-the-art methods of glaucoma classification. observed a 4% improvement in accuracy, and compared to [PERSON]'s study, there was a 28% increase in accuracy. The AUC results were within the range of previous studies (AUC 0.83-0.84). [PERSON] et al. [44] demonstrated the performance of their models on individual datasets, which is similar to our approach. However, since the test sets were augmented, the results may vary depending on the data augmentation techniques used. It is regrettable that direct comparisons were challenging due to the difficulty in determining how the data were composed, even with high accuracy. Despite various constraints, while direct comparisons were challenging, comparing results sporadically allowed us to conclude that obtaining similar or even superior results without combining multiple datasets is a meaningful analytical approach. Figure 7 shows the analysis of test data using the best-performing models from each of the input data, represented by ROC curves. REFUGE shows an improvement in performance for VROI and JET, ORIGA in BONE and GIST-HEAT, and G1020 in BONE and JET. AI-HUB demonstrates a performance enhancement in ROI.