Classification of Diabetic Retinopathy using Machine Learning
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UNIVERSIDAD POLITÉCNICA DE PUEBLA
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This paper presents a method to classify diabetic
retinopathy using fundus images. In our study we categorize the
disease into two classes: diabetic retinopathy non-proliferative
and diabetic retinopathy proliferative. The method reduces the
dimensionality of the images and find features using the statistical
method of principal component analysis (PCA). Then, we classify
the images using decision trees, the naive Bayes classifier, neural
networks, k-nearest neighbors and support vector machines. The
experimental results show that the naive Bayes classifier obtains
the best results with 73.4% of accuracy using a data set of 151
images and testing with different resolutions.
Citation
Pérez C. P., De la Calleja M. J., Medina N. M. A., Benitez R. A. 2012. Classification of Diabetic Retinopathy using Machine Learning. Universidad Politécnica de Puebla. Revista Visión Politécnica. Número 1.
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