Conference Proceeding

A Novel Machine Learning Model Based on Exudate Localization to Detect Diabetic Macular Edema

Authors: Oscar Perdomo (Universidad Nacional de Colombia, Bogotá, Colombia) , Sebastian Otalora (Universidad Nacional de Colombia, Bogotá, Colombia) , Francisco Rodríguez (National Ophthalmological Foundation, Bogotá, Colombia) , John Arevalo (Universidad Nacional de Colombia, Bogotá, Colombia) , Fabio A. González (Universidad Nacional de Colombia, Bogotá, Colombia)

Abstract

Diabetic macular edema is one of the leading causes of legal blindness worldwide. Early, and accessible, detection of ophthalmological diseases is especially important in developing countries, where there are major limitations to access to specialized medical diagnosis and treatment. Deep learning models, such as deep convolutional neural networks have shown great success in different computer vision tasks. In medical images they have been also applied with great success. The present paper presents a novel strategy based on convolutional neural networks to combine exudates localization and eye fundus images for automatic classification of diabetic macular edema as a support for diabetic retinopathy diagnosis.

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How to Cite: Perdomo, O. , Otalora, S. , Rodríguez, F. , Arevalo, J. & González, F. A. (2016) “A Novel Machine Learning Model Based on Exudate Localization to Detect Diabetic Macular Edema”, Proceedings of the Ophthalmic Medical Image Analysis International Workshop. 3(2016). doi: https://doi.org/10.17077/omia.1057