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Due to the widespread of diabetes mellitus and its associated complications, a need for early detection of the leading symptoms in the masses is felt like never before. One of the earliest signs is the presence of microaneurysms (MAs) in the fundus images. This work presents a new technique for automatic detection of MAs in color fundus images. The proposed technique utilizes Genetic Programming (GP) and a set of 28 selected features from the preprocessed fundus images in order to evolve a mathematical expression. Through the binearization of the fitness scores, the optimal expression is evolved generation by generation through a stepwise enhancement process. The best expression is then used as a classifier for real world applications. Experimental results using three publically available datasets validate the usefulness of the proposed technique and its ability to outperform the state of the art contemporary approaches.

Intelligent Automated Detection of Microaneurysms in Fundus Images Using Feature-Set Tuning