Diabetic Retinopathy Detection in Fundus Images

Diabetic retinopathy (DR) is one of the serious eye diseases and it originates from diabetes mellitus and is the most common cause of blindness in diabetic patients. Early treatment can prevent patients from being affected by this condition or at least the progression of DR can be slowed down. A key feature to recognize DR is to detect micro aneurysms (MAs) in the fundus of the eye. Micro aneurysms can be detected by excluding spurious candidates that are effectively detected using MA detector based on the combination of preprocessing methods and candidate extractors. In this work, an integrated approach is proposed for automated micro aneurysm detection with high accuracy by candidate objects which are first located by applying a dark object filtering process. Naïve Bayes Classification and examining cross-sectional images are prominently used. Newer and efficient methods are implemented to counter the drawbacks present in earlier ones. The proposed project determines the image-level classification rate of the ensemble and hence promises to be effective.