# Diabetic Macular Edema Characterization and Visualization Using Optical Coherence Tomography Images
## 4 Discussion  
The trained models returned satisfactory results, returning a mean test accuracy of 84.04% for CME, 78.44% for DRT and 95.40% for SRD, respectively. Additionally, the models tended to favor, in case of doubt, the positive class (a desirable behavior when a system is designed to be used in a medical environment). Moreover, given the robust nature of the generated visual representations, these false positives tend to be subverted in favor of the real, more clustered, detections thanks to the strategy designed to train the classifiers and sample the expert-labeled images. Compared to other works based on a segmentation, pure precision metrics seem to be inferior. However, this work cannot be compared with other works of the state of the art as it offers a completely innovative way of analyzing these regions (precisely by being able to operate even in the fuzzy regions that these other methodologies cannot really return a proper output). Furthermore, our methodology is based on a classification of a high density sampling and voting paradigm, so even these metrics based on pure hit percentage statistics are in practice significantly enhanced by this exhaustive sampling by each region of the OCT images. Figures 14 and 15 show representative examples from the test set with a healthy image and another one with different types of pathological fluid generated with the Spectralis and Cirrus devices, respectively. In addition, the labels of the expert are included to compare with the results. As can be seen, in both cases, the system is capable of satisfactorily detecting the pathological fluid accumulations whenever they are present, although we can see spurious detections in the cases of both Figures 14a and 15a. However, these detections can be easily corrected with post-processing strategies as mentioned before, but we decided to include them for a better understanding of the behavior of the system. In these visualizations, we can observe two major phenomena. The first one consists in the tendency to spurious detections in the images generated by the Spectralis of the SRD-type edema. This may be due to the higher contrast of the images generated by this device, favouring the appearance of common patterns of the SRD-type edema not present in the images that are generated with Cirrus. On the other hand, we can see a clear tendency in the Cirrus images to generate small detections of DRT-type edema. This is due to what was already mentioned in the section where we analyzed the selection of features: the DRT pattern of the images that are generated with the Cirrus device is very similar to the normal tissue in comparison with the \"wet\" and coarse texture of the DRT fluid accumulations in images that are generated with the Spectralis device. Figure 14: Test visualizations generated using images from the _Spectralis_ device. In the images that were used for testing, we have found an example of each of these extreme cases, represented in Figure 16. In this figure, we see the particular case of an Spectralis image with dark regions detected as SRD (Figure 15(a)) and a particular case of a Cirrus image with slightly thickened layers that was detected as DRT (Figure 15(b)). As a final note, currently, the main limitation of the methodology lies in the dependence on a layer extraction algorithm. Since the vitreous humor has fluid patterns similar to those found in the different classes of DME, a small failure of the layer detector is very likely to lead to false positives (as shown in Figure 16, Figure 15 particularly around the DRT detections of the binary visualization or in the example with lower overlap between samples in Figure 8). Nonetheless, these spurious detections can be compensated by adding, in the case of SRD, positional information to the feature library. A relative horizontal position descriptor would aid the trained model to better discriminate when the sample is not centered, a defining feature of this type of fluid accumulation.