Diagnosis of Eye Retinal Diseases Based on Convolutional Neural Networks Using Optical Coherence Images

In this study, the diagnosis of some diseases in the retina of the eye by using deep learning architectures is intended to be diagnosed. Optical Coherence Tomography device from Choroidal Neovascularization, Diabetic Macular Edema, Drusen and healthy eye retinal images were examined. LeNet, AlexNet and Vgg16 architectures of deep learning were used. In each architecture, the hyper parameters were changed to diagnose these diseases. Results of the implementation showed that exhibit successful results in Vgg16 and AlexNet architecture. Dropout layer structure in AlexNet has been shown to reduce the loss by minimizing loss.

I. Introduction

Optical Coherence Tomography (OCT) has become a key diagnostic imaging technique for the diagnosis of retinal diseases [1]. The ability to visualize the internal structure of retina provides a qualitative and quantitative assessment of morphological changes associated with underlying diseases [2]. Measurements derived from OCT are considered to be very important markers in evaluating treatment response and disease progression in clinical practice and in clinical trials [3], [4]. In particular, retinal thickness or central macular thickness (CMT) measured in OCT has been shown to be associated with pathological changes and treatment outcomes for various ocular diseases [5]–[7].