# Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning +
## 4 Experimental Results
### Diagnosis Accuracy  
To investigate the importance of the OD image area, the original dataset was used to create two new datasets, namely Dataset-C and Dataset-OD. The results, as obtained for the testing set and presented in Tables 5 and 6, demonstrated that the models trained on Dataset-OD were more effective than the models trained on Dataset-C, in terms of all evaluation metrics used, thus confirming that the OD image area is a clinically-important region. \begin{table}
\begin{tabular}{l r r} \hline \hline
**Model** & **Learning Rate** & **Number of Steps** \\ \hline VGG-16-B & 0.00005 & 15,000 \\ VGG-16-M & 0.00005 & 15,000 \\ ResNet-152-B & 0.00001 & 8500 \\ ResNet-152-M & 0.00001 & 8400 \\ Inception-v4-B & 0.0005 & 15,000 \\ Inception-v4-M & 0.0005 & 15,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyperparameter values. We used early stopping to alleviate overfitting. As shown by Table 5, and unlike the accuracy results obtained for the ImageNet classification task (see Table 2), the ResNet-152-M model was able to achieve the highest accuracy of 95% for Dataset-C and the highest accuracy of 96% for Dataset-OD. Compared to the images in ImageNet, the images in Dataset-C and Dataset-OD came with salient areas that had a relatively similar size and location. This homogeneity can for instance not be exploited well by InceptionNets, given the use of different convolutional filter sizes in each inception block, so as to be able to deal with salient areas that may differ from image to image in terms of size and location. Furthermore, when only considering the results presented for Dataset-OD in both Tables 5 and 6, the ResNet-152-M model was able to outperform all other models, in terms of all metrics presented. Table 6 additionally presents the sensitivity and the specificity of each model. For both metrics, the ResNet-152-M model, as trained on Dataset-OD, was able to obtain the highest values. This implies that the ResNet-152-M model was able to correctly screen both normal and glaucomatous eyes. This can also be qualitatively interpreted as follows: the ResNet-152-M model was able to provide more benefits at lower costs to patients. Furthermore, for both Dataset-C and Dataset-OD, the ResNet-152-M model obtained an AUC score of 99%, outperforming the other models (see Table 5 for the AUC score of each model). To evaluate our model generalizability, we investigated two types of learning curves during training, making use of two representative models. Figure 3 illustrates the difference between the worst- and the best-performing models. In particular, Figure 3a,c illustrates the effectiveness of training and validation in terms of accuracy, showing that the accuracy increased for both VGG-16-B and ResNet-152-M as the number of steps increased. Contrarily, in terms of loss, Figure 3b,d shows \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Classifier** & **tn** & **fp** & **tp** & **fn** & **Sensitivity** & **Specificity** \\ \hline \multirow{6}{*}{C} & VGG-16-B & 47 & 8 & 146 & 19 & 88\% & 85\% \\  & VGG-16-M & 51 & 4 & 146 & 19 & 88\% & 93\% \\  & ResNet-152-B & 53 & 2 & 141 & 24 & 85\% & 96\% \\  & ResNet-152-M & 52 & 3 & 157 & 8 & 95\% & 95\% \\  & Inception-v4-B & 49 & 6 & 148 & 17 & 90\% & 89\% \\  & Inception-v4-M & 54 & 1 & 151 & 14 & 92\% & 98\% \\ \hline \multirow{6}{*}{OD} & VGG-16-B & 33 & 22 & 157 & 8 & 95\% & 60\% \\  & VGG-16-M & 52 & 3 & 148 & 17 & 90\% & 95\% \\ \cline{1-1}  & ResNet-152-B & 55 & 0 & 137 & 28 & 83\% & 100\% \\ \cline{1-1}  & ResNet-152-M & 55 & 0 & 157 & 8 & 95\% & 100\% \\ \cline{1-1}  & Inception-v4-B & 53 & 2 & 146 & 19 & 88\% & 96\% \\ \cline{1-1}  & Inception-v4-M & 54 & 1 & 152 & 13 & 92\% & 98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Clinical evaluation of model performance for glaucoma diagnosis over the testing dataset.