Augmentation Based Diabetic Retinopathy System Using Fundus Images

Diabetic Retinopathy ailment is found in human beings causes of high glucose level that leads to vision loss with time. A prior identification of DR symptoms helps to determine the reasons and grading of diabetic retinopathy severity to provide the proper treatment to diabetic patient. Ophthalmologists checkout cotton wool spots, exudates, retinal swellings, micro-aneurysms etc in fundus images. The main work of this paper is to identify these aforementioned lesions, on fundus image dataset having three hundred thirty two images using cornea pannus based augmentation technique. In the medical image processing, Deep learning based classifier model gains better identification of these lesions. VGG-NIN model uses non-linear behavior of fundus images and grades the severity of fundus images into five classes. The performance of proposed model is achieved using parameters accuracy, sensitivity, and specificity. The results prove that proposed diabetic retinopathy grading system acquire 91.25% accuracy, 92.56% sensitivity and 94.87% specificity using 10-cross fold validation method than KNN, CNN, SVM classifier model.