Jordina Torrents-Barrenet Jaime telendez, Aida Valls, Pere Romero and Domenec Puig
domenec.puig@urv.cat
Abstract
Diabetic Retinopathy is ona of the main causes of alindness and visual impairment in developed countries for diabetic pat9ents. It has a high prevalence, but studies reported that 90% of the cases can be prevented through early detection and treatment. Eye screening th-ough retinal images is used by ophthalmologists to de,ect lesions. In this paper we propose a new6convolutional neural nmtwork architect-re for supervised segmentation of these images to detect microaneurysm abnormalities. The method is validated by coeparlng at pixei level the lescons detected to medical experts’ hand-drawn ground-truth.