# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 5 Results
### AI-HUB Dataset  
We conducted experiments using a test dataset that included 180 glaucoma images and 156 normal images. In the AI-HUB dataset, we observed improved results for ROI (accuracy 0.9435; sensitivity 0.9611), MAKO (accuracy 0.9702; sensitivity 0.9778), and GIST-HEAT (accuracy 0.9613; sensitivity 0.9778) compared to the original data (accuracy 0.9405; sensitivity 0.9444; specificity 0.9359). The final ensemble results showcased an accuracy of 0.9702, a sensitivity of 0.9722, and a specificity of 0.9679. However, there were limitations in performance enhancement within the ensemble, as five normal data images could not be correctly identified by any model.  
Specifically, in the MAKO color palette, while four misclassified images were correctly classified as normal by other input data, these classifications occurred with marginal  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & **Confusion Matrix** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** \\ \hline \multirow{2}{*}{RAW} & 52 & 7 & \multirow{2}{*}{0.9461} & \multirow{2}{*}{0.8814} & \multirow{2}{*}{0.9724} & \multirow{2}{*}{0.9286} \\  & 4 & 141 & & & & \\ \hline \multirow{2}{*}{ROI} & 51 & 8 & \multirow{2}{*}{0.9314} & \multirow{2}{*}{0.8644} & \multirow{2}{*}{0.9586} & \multirow{2}{*}{0.8947} \\  & 6 & 139 & & & & \\ \hline \multirow{2}{*}{VROI} & 54 & 5 & \multirow{2}{*}{0.9265} & \multirow{2}{*}{0.9153} & \multirow{2}{*}{0.9310} & \multirow{2}{*}{0.8438} \\  & 10 & 135 & & & & \\ \hline \multirow{2}{*}{COLORMAP (BINARY\_R)} & 54 & 5 & \multirow{2}{*}{0.9608} & \multirow{2}{*}{0.9153} & \multirow{2}{*}{0.9793} & \multirow{2}{*}{0.9474} \\  & 3 & 142 & & & & \\ \hline \multirow{2}{*}{COLORMAP (BONE)} & 57 & 2 & \multirow{2}{*}{0.9755} & \multirow{2}{*}{0.9661} & \multirow{2}{*}{0.9793} & \multirow{2}{*}{0.9500} \\  & 3 & 142 & & & & \\ \hline \multirow{2}{*}{COLORMAP (GIST\_HEAT)} & 50 & 9 & \multirow{2}{*}{0.9265} & \multirow{2}{*}{0.8475} & \multirow{2}{*}{0.9586} & \multirow{2}{*}{0.8929} \\  & 6 & 139 & & & & \\ \hline \multirow{2}{*}{COLORMAP (JET\_MPL)} & 56 & 3 & \multirow{2}{*}{0.9608} & \multirow{2}{*}{0.9492} & \multirow{2}{*}{0.9655} & \multirow{2}{*}{0.9180} \\  & 5 & 140 & & & & \\ \hline \multirow{2}{*}{COLORMAP (MAKO)} & 55 & 4 & \multirow{2}{*}{0.9657} & \multirow{2}{*}{0.9322} & \multirow{2}{*}{0.9793} & \multirow{2}{*}{0.9483} \\  & 3 & 142 & & & & \\ \hline \multirow{2}{*}{Ensemble} & 58 & 1 & \multirow{2}{*}{0.9853} & \multirow{2}{*}{0.9831} & \multirow{2}{*}{0.9862} & \multirow{2}{*}{0.9667} \\  & 2 & 143 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summarized glaucoma classification result of G1020.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & **Confusion Matrix** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** \\ \hline \multirow{2}{*}{COLORMAP (JET\_MPL)} & 9 & 8 & \multirow{2}{*}{0.7231} & \multirow{2}{*}{0.5294} & \multirow{2}{*}{0.7917} & \multirow{2}{*}{0.4737} \\  & 10 & 38 & & & & \\ \hline \multirow{2}{*}{COLORMAP (MAKO)} & 8 & 9 & \multirow{2}{*}{0.6923} & \multirow{2}{*}{0.4706} & \multirow{2}{*}{0.7708} & \multirow{2}{*}{0.4211} \\  & 11 & 37 & & & & \\ \hline \multirow{2}{*}{Ensemble} & 13 & 4 & \multirow{2}{*}{0.8308} & \multirow{2}{*}{0.7647} & \multirow{2}{*}{0.8542} & \multirow{2}{*}{0.6500} \\  & 7 & 41 & & & & \\ \hline \end{tabular}
\end{table}
Table 3: _Cont_.  
probability values and, therefore, did not significantly influence the overall ensemble score. This indicates a potential area for further refinement in the ensemble methodology to better incorporate subtle classifications into the final results. This is shown in Table 5.