# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 5 Results
### Private Dataset  
An unpublished dataset consisting of 30 normal images and 29 glaucoma images was used for testing. Compared to the original data (accuracy 0.8305; sensitivity 0.7879), there was no significant improvement in the classification of glaucoma across any input model. However, there was an improvement (5.51%) in the accuracy of the normal data (specificity 0.8846 to 0.9333), leading to final results of an accuracy of 0.9322, a sensitivity of 0.9310, and a specificity of 0.9333, as shown in Table 6.  
Given the nature of the data from a private clinic, where the majority are normal cases and glaucoma-suspect patients often visit temporarily, it was challenging for physicians to make definitive diagnoses of glaucoma based on these images without observing the progression of the condition. Therefore, it should be noted that the glaucoma images in this dataset were composed of cases where patients had repeatedly visited over several years and were definitively diagnosed with glaucoma, representing a small subset of all patients. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & **Confusion Matrix** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** \\ \hline \multirow{2}{*}{RAW} & 26 & 3 & \multirow{2}{*}{0.8305} & \multirow{2}{*}{0.7879} & \multirow{2}{*}{0.8846} & \multirow{2}{*}{0.8966} \\  & 7 & 23 & & & & \\ \hline \multirow{2}{*}{ROI} & 27 & 2 & \multirow{2}{*}{0.8644} & \multirow{2}{*}{0.8182} & \multirow{2}{*}{0.9231} & \multirow{2}{*}{0.9310} \\  & 6 & 24 & & & & \\ \hline \multirow{2}{*}{VROI} & 25 & 4 & \multirow{2}{*}{0.7797} & \multirow{2}{*}{0.7353} & \multirow{2}{*}{0.8400} & \multirow{2}{*}{0.8621} \\  & 9 & 21 & & & & \\ \hline \multirow{2}{*}{COLORMAP (BINARY\_R)} & 27 & 2 & \multirow{2}{*}{0.8475} & \multirow{2}{*}{0.7941} & \multirow{2}{*}{0.9200} & \multirow{2}{*}{0.9310} \\  & 7 & 23 & & & & \\ \hline \multirow{2}{*}{COLORMAP (BONE)} & 27 & 2 & \multirow{2}{*}{0.8475} & \multirow{2}{*}{0.7941} & \multirow{2}{*}{0.9200} & \multirow{2}{*}{0.9310} \\  & 7 & 23 & & & & \\ \hline \multirow{2}{*}{COLORMAP (GIST\_HEAT)} & 26 & 3 & \multirow{2}{*}{0.8136} & \multirow{2}{*}{0.7647} & \multirow{2}{*}{0.8800} & \multirow{2}{*}{0.8966} \\  & 8 & 22 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summarized glaucoma classification result of private dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & **Confusion Matrix** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** \\ \hline \multirow{2}{*}{RAW} & 170 & 10 & 0.9405 & 0.9444 & 0.9359 & 0.9444 \\  & 10 & 146 & & & & \\ \hline \multirow{2}{*}{ROI} & 173 & 7 & \multirow{2}{*}{0.9435} & \multirow{2}{*}{0.9611} & \multirow{2}{*}{0.9231} & \multirow{2}{*}{0.9351} \\  & 12 & 144 & & & & \\ \hline \multirow{2}{*}{VROI} & 169 & 11 & \multirow{2}{*}{0.9345} & \multirow{2}{*}{0.9389} & \multirow{2}{*}{0.9295} & \multirow{2}{*}{0.9389} \\  & 11 & 145 & & & & \\ \hline \multirow{2}{*}{COLORMAP (BINARY\_R)} & 172 & 8 & \multirow{2}{*}{0.9554} & \multirow{2}{*}{0.9556} & \multirow{2}{*}{0.9551} & \multirow{2}{*}{0.9609} \\  & 7 & 149 & & & & \\ \hline \multirow{2}{*}{COLORMAP (BONE)} & 170 & 10 & \multirow{2}{*}{0.9464} & \multirow{2}{*}{0.9444} & \multirow{2}{*}{0.9487} & \multirow{2}{*}{0.9551} \\  & 8 & 148 & & & & \\ \hline \multirow{2}{*}{COLORMAP (GIST\_HEAT)} & 176 & 4 & \multirow{2}{*}{0.9613} & \multirow{2}{*}{0.9778} & \multirow{2}{*}{0.9423} & \multirow{2}{*}{0.9514} \\  & 9 & 147 & & & & \\ \hline \multirow{2}{*}{COLORMAP (JET\_MPL)} & 168 & 12 & \multirow{2}{*}{0.9375} & \multirow{2}{*}{0.9333} & \multirow{2}{*}{0.9423} & \multirow{2}{*}{0.9492} \\  & 9 & 147 & & & & \\ \hline \multirow{2}{*}{COLORMAP (MAKO)} & 176 & 4 & \multirow{2}{*}{0.9702} & \multirow{2}{*}{0.9778} & \multirow{2}{*}{0.9615} & \multirow{2}{*}{0.9670} \\  & 6 & 150 & & & & \\ \hline \multirow{2}{*}{Ensemble} & 175 & 5 & \multirow{2}{*}{0.9702} & \multirow{2}{*}{0.9722} & \multirow{2}{*}{0.9679} & \multirow{2}{*}{0.9722} \\  & 5 & 151 & & & & \\ \hline \hline \end{tabular}
\end{table}