Abstract

Aim: Diabetic Retinopathy (DR) is one of the major problems of diabetic patients. The diabetic patient is not aware of any symptom until it is too late for effective treatment. It is the leading cause of blindness. Diabetic retinopathy results in retinal disorders that include Microaneurysms (MA), soft exudates, hard exudates and intra-retinal vascular abnormalities. Methods: Soft Computing Neural Networks are used to detect and diagnose lesions or abnormalities associated with diabetic retinopathy which facilitate the Ophthalmologists in accurate diagnosis and early treatment to prevent vision loss in diabetic patients. Results: The result shows that the methodology used is well suited for the early diagnosis of the diabetic retinopathy disease. Conclusions: By evaluating the exudates and fovea region, and analyzing the relation between them, the severity of DR can be easily identified to prevent vision loss in diabetic patients.

 

Keywords

Diabetic Retinopathy (DR), Microaneursym (MA), Exudates, Fovea, Diagnosis