Computer aided diagnostic system for grading of diabetic retinopathy

The automated detection and diagnosis of Diabetic Retinopathy (DR) is very critical to save the patient's vision and to help the ophthalmologists in mass screening of diabetes sufferers. DR is a progressive eye disease and should be detected as early as possible. In this paper, we present a new system for detection and classification of different DR lesions i.e. Microaneurysms (MAs), Haemorrhage (H), Hard Exudates (HE) and Cotton Wool Spots (CWS). We proposed a three stage system in which first stage extracts all possible candidate lesions present in a fundus image suing filter bank. Then feature sets are computed for each candidate lesion using different properties and features followed by classification of lesions. The evaluation of proposed system is performed using retinal image databases with the help of different performance matrices and the results show the validity of proposed system.