Application of Deep Convolutional Neural Networks VGG-16 and GoogLeNet for Level Diabetic Retinopathy Detection

Abstract

Diabetic retinopathy (DR) is a diabetes complication that damages the retina. This type of medical condition affects up to 80% of patients with diabetes for 10 or more years. The expertise and equipment required are often lacking in areas where diabetic retinopathy detection is most needed. Most of the work in the field of diabetic retinopathy has been based on disease detection or manual extraction of features. Thus, this research aims at automatic diagnosis of the disease in its different stages using deep learning neural network approach. This paper presents the design and implementation of Graphic Processing Unit (hereby GPU) accelerated deep convolutional neural networks to automatically diagnose and thereby classify high-resolution retinal images into five stages of the disease based on its severity. The accuracy of the single model convolutional neural networks presented in this paper is 71.65% from VGG-16.