# Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning +
## 4 Experimental Results
### Diagnosis Accuracy
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data** & **Classifier** & **Accuracy** & **Recall** & **Precision** & **F1 Score** & **AUC Score** \\ \hline \multirow{6}{*}{C} & VGG-16-B & 88\% & 88\% & 95\% & 91\% & 93\% \\  & VGG-16-M & 90\% & 88\% & 97\% & 92\% & 94\% \\  & ResNet-152-B & 88\% & 85\% & 99\% & 91\% & 94\% \\  & ResNet-152-M & 95\% & 95\% & 98\% & 96\% & 99\% \\  & Inception-v4-B & 90\% & 90\% & 96\% & 93\% & 97\% \\  & Inception-v4-M & 93\% & 92\% & 99\% & 95\% & 99\% \\ \hline \multirow{6}{*}{OD} & VGG-16-B & 86\% & 95\% & 88\% & 91\% & 93\% \\  & VGG-16-M & 91\% & 90\% & 98\% & 94\% & 96\% \\ \cline{1-1}  & ResNet-152-B & 87\% & 83\% & 100\% & 91\% & 95\% \\ \cline{1-1}  & ResNet-152-M & 96\% & 95\% & 100\% & 97\% & 99\% \\ \cline{1-1}  & Inception-v4-B & 91\% & 88\% & 99\% & 93\% & 97\% \\ \cline{1-1}  & Inception-v4-M & 94\% & 92\% & 99\% & 95\% & 99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of model performance for glaucoma diagnosis over the testing dataset. that the VGG-16-B model did not optimize well at later steps, resulting in overfitting, while the ResNet-152-M model was able to converge well, resulting in good fitting. Finally, we made use of RIM-ONE r3 [28], an external dataset, to provide a more in-depth evaluation of the generalization power of ResNet-152-M, our most effective model. The total number of fundus images in the RIM-ONE r3 dataset is 159, of which 85 eye images are normal and 74 eye images are glaucomatous. Although the characteristics (angle, eye range, camera model) of the fundus images are different from the fundus images collected at Samsung Medical Center, in general affecting the diagnosis accuracy of deep learning models, we applied our ResNet-152-M model, as trained on Dataset-C, to the RIM-ONE r3 dataset, given the visual resemblance between the two different datasets. The authors of [17] performed experiments using five-fold cross-validation, with each validation set containing 17 normal and 14 glaucomatous eye images. For our external testing purposes, we split the RIM-ONE r3 dataset into five subsets, randomly selecting 31 images for each subset. The authors of [17] then evaluated the overall accuracy, sensitivity, and specificity. We implemented the same experiment, and the results obtained are summarized in Table 7. Note that the models proposed in [17] may be biased towards the RIM-ONE r3 dataset (the authors of [17] did not provide testing results for a dataset different from the RIM-ONE r3 dataset). Moreover, as also mentioned in [17], the small size of the RIM-ONE r3 dataset made it challenging to apply CNNs, or even most other machine learning techniques. Figure 3: Comparison between the models with the lowest and the highest effectiveness for the training set and the validation set during fine-tuning. (**a**) VGG-16-B accuracy on Dataset-C. (**b**) VGG-16-B training and validation (_generalization_) error on Dataset-C. (**c**) ResNet-152-M accuracy on Dataset-OD. (**d**) ResNet-152-M training and validation error on Dataset-OD.