In this paper, we present a novel approach to microaneurysm candidate extraction. To strengthen the accuracy of individual algorithms, we propose an ensemble of state-of-the-art candidate extractors. We apply a simulated annealing based method to select an optimal combination of such algorithms for a particular dataset. We also present a novel classification technique, which is based on a parallel ensemble of kernel density estimators. The experimental results show improvement in the positive likelihood rate compared to the individual candidate extractors. © 2012 Springer-Verlag.
CITATION STYLE
Antal, B., Lázár, I., & Hajdu, A. (2012). An ensemble approach to improve microaneurysm candidate extraction. In Communications in Computer and Information Science (Vol. 222 CCIS, pp. 378–391). https://doi.org/10.1007/978-3-642-25206-8_25
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