Comparative Exudate Classification Using Support Vector Machines and Neural Networks
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
GA-facilitated classifier optimization with varying similarity measures
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Identification of different stages of diabetic retinopathy using retinal optical images
Information Sciences: an International Journal
ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology
Extraction of visual features with eye tracking for saliency driven 2D/3D registration
Image and Vision Computing
Artificial Intelligence Techniques for Medical Image Analysis: Basics, Methods, Applications
Artificial Intelligence Techniques for Medical Image Analysis: Basics, Methods, Applications
Editorial Recent Advances in Cognitive Informatics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Fuzzy approaches are one of the widely used artificial intelligence techniques in the field of ophthalmology. These techniques are used for classifying the abnormal retinal images into different categories that assist in treatment planning. The main characteristic feature that makes the fuzzy techniques highly popular is their accuracy. But, the accuracy of these fuzzy logic techniques depends on the expertise knowledge, which indirectly relies on the input samples. Insignificant input samples may reduce the accuracy that further reduces the efficiency of the fuzzy technique. In this work, the application of Genetic Algorithm GA for optimizing the input samples is explored in the context of abnormal retinal image classification. Abnormal retinal images from four different classes are used in this work and a comprehensive feature set is extracted from these images as classification is performed with the fuzzy classifier and also with the GA optimized fuzzy classifier. Experimental results suggest highly accurate results for the GA based classifier than the conventional fuzzy classifier.