Iago Otero, Plácido L. Vidal, Joaquim de Moura, Jorge Novo Buján , Marcos Ortega Hortas
The methodology presented in this paper aims to detect pathological regions affected by one or more of the three clinically defined types of Diabetic Macular Edema (DME). Using representative samples extracted from Optical Coherence Tomography (OCT) images, three representative classifiers are trained to analyze new input images and create an intuitive visualization of the detection results. The trained models provided a satisfactory performance for all three defined types of DME, andthe visual feedback can effectively assists clinical experts in the diagnosis of this representative and extended disease.
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