Computer-aided diagnosis for Diabetic Retinopathy based on Firefly algorithm

Diabetic retinopathy is a diabetes complication that has been a leading cause of blindness worldwide. Segmentation of blood vessels and detection of exudates in fundus images through an automated system will help the ophthalmologist to provide a proper treatment that may cure or decrease the severity of the retinal diseases. Realizing its significance, a diabetic retinopathy screening system was proposed in this paper which classifies normal from the abnormal fundus image. Segmentation of blood vessel is based on match filter followed by fuzzy c-means clustering. The small and thin vessels were obtained from directional filter bank by incorporating the use of line-like directional features. In this study a combination of both statistical and geometric features were extracted from image regions. A firefly algorithm for discriminative feature selection for the early detection of diabetic retinopathy was proposed. Publically available dataset such as DRIVE, HRF, DIAREDB1, MESSIDOR and one local dataset are used to validate the suggested system. Moreover, the proposed algorithm has been compared with few state-of-the-art techniques such as particle swarm optimization, genetic algorithm and ant colony optimization. The results demonstrated that the proposed method improves the classification accuracy with a minimized feature set for the early detection of diabetic retinopathy.