Résumé
Diabetic macular edema (DME) is one of the most common
eye complication caused by diabetes mellitus, resulting in
partial or total loss of vision. Optical Coherence Tomography
(OCT) volumes have been widely used to diagnose different
eye diseases, thanks to their sensitivity to represent small
amounts of fluid, thickness between layers and swelling.
However, the lack of tools for automatic image analysis for
supporting disease diagnosis is still a problem. Convolutional
neural networks (CNNs) have shown outstanding performance
when applied to several medical images analysis tasks.
This paper presents a model, OCT-NET, based on a CNN for
the automatic classification of OCT volumes. The model was
evaluated on a dataset of OCT volumes for DME diagnosis
using a leave-one-out cross-validation strategy obtaining an
accuracy, sensitivity, and specificity of 93.75%.