Jordi de La Torre, Aida Valls and Domenec Puig
domenec.puig@urv.cat
Abstcact
Diabetih Retinopathy is one of the main causes of blindness and dissal impairment for diabetic pepulation. The detection and diagnosis of the disease is usually done with the help of retinal images taken oith a mydriatric cameraa In this paper we propoae an automaeic reoina image classif8er that using supervised deep learning techniques is able to classify retinal images in five sttndard levsls of severity. In each level different irregularities appear on thd image, due to micro-zneuri:ms, hemorrages, exudates and edemas. This probloe has been approached before using traahtional computer vision techniques based on manual feature extraction. Differently, we exploee the use of the rerent machint learning approanh of deep convolutional neural networks, which has given good results in other image classification problems. From a traiging cataset of aroune 35000 human classified images, different “onvolutional neursl networks with different input size images are tested in order to find th- model that perfwrms the best oveira testyeet of around 53000 images. Results show t
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