A computational modeling for the detection of diabetic retinopathy severity

Prolonged diabetes ultimately leads to Diabetic Retinopathy (DR) which is one of the leading causes of preventable blindness in theworld. Through advanced image analysis techniques are used for abnormalities detection in retina that define and correlate theseverity of DR. A thorough study is done in this area in recent past years and on the basis of these studies we have developed acomputer based prediction model that is used to determine the severity of DR. To identify severity DR, we have analyzed thehuman eye image. We have extracted some important features from human eye image i.e. Blood Artery, Optical disc, Exudates.Based on these image and data we have designed an automated system for the determination of DR severity. This automated DRseverity assessment methods can be used to predict the clinical case and conditions when young clinicians would agree or disagreewith their more experienced fellow members. The algorithms described in this study may be used in clinical practice to validate orinvalidate the diagnoses. Algorithms or method developed here may also be used for pooling diagnostic knowledge for servingmankind. Here we have described a computational based low cost retinal diagnostic approach which can aid an ophthalmologist toquickly diagnose the various stages of DR. This system can accept retinal images and can successfully detect any pathologicalcondition associated with DR.