Data Augmentation For Improving Proliferative Diabetic Retinopathy Detection In Eye Fundus Images Using Machine Learning Techniques

Abstract

Nowadays, some of the most common causes of visual impairment and blindness are diabetic retinopathy, glaucoma, hypertension and macular degeneration. Therefore computer aided automated diagnosis approaches have great potential in clinical to accurately detect DR in a short time which can further help to improve the screening rate of DR and reduce the number of blindness. For a
deep learning model, the most important parts that should be focused on are data set, network architecture and training method. Before being used to train our model, fundus images data set obtained from public resources is preprocessed and segmented. In the proposed system OD (Optic Disk) detection based on structured learning which belongs to a supervised method to avoid making
assumptions. The proposed method utilizes the edge information of the fundus image to detect the OD. Finally the deep neural network (DNN) classifier is used to check whether the fundus image is cancerous or non-cancerous.