A Novel System for the Identification of Diabetic Retinopathy using Computer Based Statistical Classification

Diabetes Retinopathy (DR) is an eye disorder that affects the human retina due to increased insulin levels in blood. Early detection and diagnosis of DR essential optimal treatment diabetic patients. The current research develop controls for identifying different characteristics differences colour using classifications. This therapeutic approach describes diabetes recovery from data collected multiple fields including DRIDB0, DRIDB1, MESSIDOR, STARE HRF. Here machine learning, neural networks deep learning algorithms issues are addressed with related topics such as Sensitivity, Precision, Accuracy, Error, Specificity F1-score, Mathews Correlation Coefficient (MCC) coefficient kappa compared. Finally strategy results were more effective compared other methods. system can help ophthalmologists, identify symptoms at early stage, better improve quality life biology.