Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach

Human eye is one of the most sophisticated organ, with retina, pupil, iris cornea, lens and optic nerve. Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Uncontrolled diabetes retinopathy (DR) and glaucoma may lead to blindness. DR is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of DR are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources used for mass screening of DR. We present an automatic screening system for the detection of normal and DR stages (NPDR and PDR). The proposed systems involves processing of fundus images for extraction of abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture and entropies. Our protocol uses total of 156 subjects consisting of two stages of DR and normal. In this work, we have fed thirteen statistically significant (p