Abstract
We present in this paper a novel hybrid statistical framework for retinal image classification and diabetic retinopathy detection. Our purpose here is to develop a probabilistic SVM-based kernel combined with a finite mixture of Scaled Dirichlet distributions. The developed method offers more flexibility in data modeling and classification since it takes advantage of both generative and discriminative models. Quantitative results obtained from a large dataset of real retinal images confirm the effectiveness of the proposed framework.