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

• Published in: Knowledge-based systems Vol. 39; pp. 9 - 22
• Main Authors: Mookiah, M.R.K., Acharya, U. Rajendra, Martis, Roshan Joy, Chua, Chua Kuang, Lim, C.M., Ng, E.Y.K., Laude, Augustinus
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2013
• Subjects: Bifurcation point; Blood vessels; Diabetic retinopathy; Evolutionary algorithm; Laws texture energy; Local binary pattern; Bifurcation point; Laws texture energy; Diabetic retinopathy; Local binary pattern; Evolutionary algorithm
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2012.09.008

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<0.0001) features for Probabilistic Neural Network (PNN), Decision Tree (DT) C4.5, and Support Vector Machine (SVM) to select the best classifier. The best model parameter (σ) for which the PNN classifier performed best was identified using global optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). We demonstrated an average classification accuracy of 96.15%, sensitivity of 96.27% and specificity of 96.08% for σ=0.0104 using threefold cross validation using PNN classifier. The computer-aided diagnosis (CAD) results were validated by comparing with expert ophthalmologists. The proposed automated system can aid clinicians to make a faster DR diagnosis during the mass screening of normal/DR images.