# Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning +
## 4 Experimental Results
### Localization of glaucomatous Areas  
Unlike the evaluation of diagnosis accuracy, it is difficult to perform a quantitative evaluation of the effectiveness of localization, given that boundaries of optic disc notching or retinal nerve fiber layer defects cannot be clearly segmented in eye fundus images. In other words, there is no absolute gold standard available for a quantitative analysis of glaucomatous defect localization. Indeed, clinicians typically only confirm the presence of glaucoma by observing structural features [2; 52; 53]. Therefore, we made use of a qualitative approach towards evaluating the effectiveness of localization, asking clinicians for their informed opinion. Figure 4 shows the localization results obtained by the *-M models for a fundus image taken from Dataset-C that was diagnosed as glaucomatous. For each model, the layer extracted for the purpose of localization was the last convolutional layer before the global max-pooling layer. Even though all classifiers correctly identified the given fundus image as glaucomatous, the location each network looked at was different, with the VGG-16-M model succeeding in localizing the correct glaucomatous area in the given input image (as shown in Figure 4b). On the contrary, ResNet-152-M and Inception-v4-M were able to localize the glaucomatous area correctly. Given both Figure 4c and Figure 4d, the ResNet-152-M model produced a more precise localization than the Inception-v4-M model, with the produced localization also being more correct from a clinical point of view. Figures 5 and 6 visualize how models find the glaucomatous area per ResNet block in ResNet-152-M. Given both figures, we can again confirm that our model looked at the OD area to diagnose glaucoma. Having a look at the internals of ResNet-152-M, as illustrated in Figure 5, the leftmost image was the input image, and from the left to the right, we present the last convolutional layer of each residual block. As the input went from the lower layers to the higher layers, Figure 5 shows where the model looked at in order to make a classification decision, localizing where the glaucomatous region can be found in the heat map. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Ours** & **ES [17]** & **CNN [17]** & [50] & [51] & [24] \\ \hline Accuracy & 93.5 & 94.1 & 92.4 & 90.2 & 89.4 & 92.1 \\ Sensitivity & 92.9 & 92.3 & 90.1 & 87.4 & 86.4 & 89.8 \\ Specificity & 92.9 & 95.6 & 94.3 & 92.5 & 92.0 & 94.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Effectiveness of glaucoma diagnosis for RIM-ONEr3 [28]. Ours refers to the ResNet-152-M model. The ES model of [17] model used Entropy Sampling for feature extraction, whereas the CNN of [17] used a CNN. Both ES [17] and CNN [17] used a softmax classifier. The results were adopted from [17]. Figure 4: Localization results produced by the different models for the input image shown in (**a**). (**b**) shows the (wrong) localization result produced by VGG-16-M. (**c**) shows the (correct) localization result produced by Inception-v4-M, and (**d**) shows the (correct) localization result produced by ResNet-152-M. Both Figures 7 and 8 show localization results obtained for our most effective model, namely ResNet-152-M, for fundus images taken from Dataset-C. In our test results, 209 images were correctly diagnosed and 11 images were wrongly diagnosed, given the human labels. Among the correctly-diagnosed images, the optic nerves in the true positives were all correctly localized, as shown in Figure 7a. For the true negatives, most of them were clinically well localized, as shown in Figure 7b. However, there were some atypical localizations, as indicated by the white arrows in Figure 7c. Among the wrongly-classified images, there were three false positives and three false negatives. Regarding the false positives, each one of them was diagnosed wrongly for different reasons. In Figure 8a, we can observe the distribution of the normal retinal nerve fibers around each optic disc.