Diabetic Retinopathy Detection: Solutions Through Application of Meta-Heuristic Approaches

Abstract

In recent times, occurrences of diabetes related disorders is experiencing a steep climb with most of the symptoms being asyptomatic. One such disorder which affects the human eye is Diabetic Retinopathy (DR). DR is being diagnosed only at later stages which are quite critical. These last stages may result in eventual blindness as DR tends to cause damage to the retina behind the human eye. Hence, early detection aids in appropriate treatment resulting in prevention before its occurrence. Early detection methods involve processing with optical coherence tomography images (OCT) or the well-known fundus images which require image processing tehcniques in both cases. A case of retinal fundus image processing is investigated in this paper with more emphasis on adopting nature inspired algorithms for improved accuracy of detection. Apart from their well-known traits of fast convergence, their closeness to real time applications and events, they aid in optimal selection of features which is critical phase on the segmentation and detection process. A few well-known meta-heuristic methods have been investigated in this paper projecting their merits and limtiations to aid in future research.