Feature Convergence for Diabetic Retinopathy Detection Based on Activated Convolution Networks Using Fundus Images

Diabetic retinopathy (DR) causes vision blindness due to retinal impairment over prolonged high blood glucose levels.Pre-diagnosis of glucose levels and detection of impairments reduce the risk of DR.Image-based diagnosis and detection are widely adopted in modern clinical assessments, aided by computerized algorithms.A Converging Feature Classification Method (CFCM) is proposed to reduce the false rates in diagnosing DR using optical eye images.This method utilizes an activated convolution neural network (A-CNN) to reduce false rates.The activation process is the normalization of extracted features by detaining the replicated ones.Such replications are prevented from increasing the false rate through the hidden computing layers of the CNN.The normalized CNN trains the hidden layer by identifying false (replicated) features and extracting unique features for DR detection.Similarly, the extracted unique features are aligned with the training images to find the exact match of DR.The training is improved through replicated and non-replicated features to ensure high precision in DR detection is achieved.