# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 5 Results
### ORIGA Dataset  
The ORIGA dataset is known in other studies as well for showing low accuracy due to many of its images being darkly photographed, making it challenging to discern structural features based on contrast. We conducted experiments using a test dataset that included 17 glaucoma images and 48 normal images. In most studies using this dataset, the accuracy results ranged from 0.64 to 0.76. In our research, using the original data for training yielded an accuracy of 0.7231, a specificity of 0.8125, but a notably low sensitivity of 0.4706 compared to other datasets. However, with the BONE and GIST-HEAT color palettes, the sensitivity improved to over 0.7.  
The final ensemble model in our study showed an accuracy of 0.8308, a sensitivity of 0.7647, and a specificity of 0.8542, achieving a performance improvement of 2% to 14% compared to previous studies. It has been established that the ORIGA dataset is particularly challenging for achieving high accuracy, especially in detecting glaucoma with low sensitivity (0.4706, 0.5294), confirming its status as a challenging dataset. When comparing the results with the retina fundus images, there was a dramatic increase in performance, with accuracy improving by 11% and sensitivity increasing by an impressive 29%. This is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & **Confusion Matrix** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** \\ \hline \multirow{2}{*}{RAW} & 33 & 7 & 0.9725 & 0.8250 & 0.9889 & 0.8919 \\  & 4 & 356 & & & & \\ \hline \multirow{2}{*}{ROI} & 31 & 9 & 0.9575 & 0.7750 & 0.9778 & 0.7949 \\  & 8 & 352 & & & & \\ \hline \multirow{2}{*}{VROI} & 34 & 6 & 0.9600 & 0.8500 & 0.9722 & 0.7727 \\  & 10 & 350 & & & & \\ \hline \multirow{2}{*}{COLORMAP (BINARY\_R)} & 32 & 8 & 0.9750 & 0.8000 & 0.9944 & 0.9412 \\  & 2 & 358 & & & & \\ \hline \multirow{2}{*}{COLORMAP (BONE)} & 33 & 7 & 0.9675 & 0.8250 & 0.9833 & 0.8462 \\  & 6 & 354 & & & & \\ \hline \multirow{2}{*}{COLORMAP (GIST\_HEAT)} & 30 & 10 & 0.9575 & 0.7500 & 0.9806 & 0.8108 \\  & 7 & 353 & & & & \\ \hline \multirow{2}{*}{COLORMAP (JET)} & 35 & 5 & 0.9775 & 0.8750 & 0.9889 & 0.8974 \\  & 4 & 356 & & & & \\ \hline \multirow{2}{*}{COLORMAP (MAKO)} & 25 & 15 & 0.9625 & 0.6250 & 1.0000 & 1.0000 \\  & 0 & 360 & & & & \\ \hline \multirow{2}{*}{Ensemble} & 36 & 4 & 0.9875 & 0.9000 & 0.9972 & 0.9730 \\  & 1 & 359 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summarized glaucoma classification result of REFUGE.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & **Confusion Matrix** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** \\ \hline \multirow{2}{*}{RAW} & 8 & 9 & 0.7231 & 0.4706 & 0.8125 & 0.4706 \\  & 9 & 39 & & & \\ \hline \multirow{2}{*}{ROI} & 9 & 8 & 0.7846 & 0.5294 & 0.8750 & 0.6000 \\  & 6 & 42 & & & & \\ \hline \multirow{2}{*}{VROI} & 7 & 10 & 0.7231 & 0.4118 & 0.8333 & 0.4667 \\  & 8 & 40 & & & & \\ \hline \multirow{2}{*}{COLORMAP (BINARY\_R)} & 6 & 11 & 0.6462 & 0.3529 & 0.7500 & 0.3333 \\  & 12 & 36 & & & & \\ \hline \multirow{2}{*}{COLORMAP (BONE)} & 12 & 5 & 0.8000 & 0.7059 & 0.8333 & 0.6000 \\  & 8 & 40 & & & & \\ \hline \multirow{2}{*}{COLORMAP (GIST\_HEAT)} & 13 & 4 & 0.8030 & 0.7647 & 0.8163 & 0.5909 \\  & 9 & 40 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summarized glaucoma classification result of ORIGA.