LBP and Machine Learning for Diabetic Retinopathy Detection
| Autor: | Ma. Auxilio Medina, Lourdes Tecuapetla, Jorge de la Calleja, Argelia Berenice Urbina Nájera, Everardo Bárcenas |
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| Rok vydání: | 2014 |
| Předmět: |
Retina
genetic structures Artificial neural network Local binary patterns business.industry Eye disease Pattern recognition Diabetic retinopathy medicine.disease eye diseases Random forest Support vector machine medicine.anatomical_structure Diabetes mellitus medicine sense organs Artificial intelligence business |
| Zdroj: | Intelligent Data Engineering and Automated Learning – IDEAL 2014 ISBN: 9783319108391 IDEAL |
| DOI: | 10.1007/978-3-319-10840-7_14 |
| Popis: | Diabetic retinopathy is a chronic progressive eye disease associated to a group of eye problems as a complication of diabetes. This disease may cause severe vision loss or even blindness. Specialists analyze fundus images in order to diagnostic it and to give specific treatments. Fundus images are photographs taken of the retina using a retinal camera, this is a noninvasive medical procedure that provides a way to analyze the retina in patients with diabetes. The correct classification of these images depends on the ability and experience of specialists, and also the quality of the images. In this paper we present a method for diabetic retinopathy detection. This method is divided into two stages: in the first one, we have used local binary patterns (LBP) to extract local features, while in the second stage, we have applied artificial neural networks, random forest and support vector machines for the detection task. Preliminary results show that random forest was the best classifier with 97.46% of accuracy, using a data set of 71 images. |
| Databáze: | OpenAIRE |
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