# Diabetic Macular Edema Characterization and Visualization Using Optical Coherence Tomography Images
## 3 Results
### Training of the Models  
In this section we present the results of the training steps divided into the three different types of DME. In Figure 11 we can see the results of the CME training process. On the other hand, in Figure 12 we present the results for the training of the model destined to classify DRT samples. Finally, in Figure 13, the results for the training of the model to detect SRD samples.  
To increase the training speed and the model selection, the increments were done in steps of three features instead of one. As such, in these figures, the real mean test accuracy presented is the one of the closest feature subset to the estimated best candidate (hence the \"Closest mean test accuracy\" tag).  
The final selected model for each type of DME can be seen in Table 2. In particular, we decided to use the RF instead of the SVM-RBF for the DRT class as, despite the slightly higher closer mean test accuracy, we opted to use a simpler model with 44 features instead of a much more complex and only slightly better model with 99 features. Additionally, if we observe the fitted curves, we can see that the slope (that is, improvement provided by each new feature) is negligible in this ME class.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CME** & **DRT** & **SRD** \\ \hline N\({}^{\text{o}}\) of features & 32 & 44 & 28 \\ Classifier Type & SVM-RBF & RF & LDC \\ Estimated accuracy & 83.83\% & 78.44\% & 95.69\% \\ Closest mean test acc. & 84.04\% & 78.94\% & 95.40\% \\ Standard Deviation & 3.67\% & 3.64\% & 2.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Final results for each type of ME and chosen model.  
Figure 10: Top 100 features that were selected for each of the considered DME types.  
Figure 11: CME cross validation training results per feature subset and fitted curve used to select the best model.  
Figure 12: DRT cross validation training results per feature subset and fitted curve used to select the best model.  
Figure 13: SRD cross validation training results per feature subset and fitted curve used to select the best model.  
Looking at the Top 100 features generated by the SFS algorithm, we can see the reason behind this behavior. As shown in Figure 10, for the DRT class, the selector was only able to find a few features at the beginning that were strictly better than the rest. Afterwards, the selector has chosen to directly include whole families of features instead of individual markers since there is not enough difference in relevance between them to decide on a particular one. For this reason, the training results start at an already high accuracy for this class and then remain almost unchanged (despite being nearly monotonic).  
On the other hand, we see a similar behaviour in the case of the SRD, but for very different reasons. In this case, it is due to the fact that, since it is an easier DME type to identify than the other cases, a smaller number of features is enough for its detection and classification (despite the fact that all of them contribute sensibly to the variance of the dataset).