Abstract
Nowadays, Diabetic Macular Edema (DME) is one of the leading causes of blindness in developed countries, and its characterized by the presence of pathological fluid accumulations inside the retinal layers. Currently, the main way to detect these fluid accumulations (as well as their severity) is through the use of Optical Coherence Tomography (OCT) imaging. In particular, this ophthalmological image modality allows a precise non-invasive analysis of the morphology of the retina and its structures. Due to the complexity of attempting to successfully segment these fluid accumulations, an alternative paradigm for their detection has been recently proposed. This paradigm, based on a diffuse representation of the pathological regions, creates an intuitive representation of the pathological regions based on a confidence map. Currently, there are only two approaches for this paradigm: one based on a predefined library of texture and intensity features with established machine learning algorithms and other based on deep learning methods. Both approaches have proven to offer satisfactory results, but each one of them performs better in different scenarios. In this work, we perform a complete analysis and comparison on the behaviour and performance of both strategies in a clinical screening scenario to evaluate the suitability of both approaches for the clinical practice as well as their performance as computer vision strategies.