Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

Rubina Sarki,Hua Wang,Khandakar Ahmed,Yanchun Zhang

doi:10.1109/access.2020.3015258

Abstract

Diabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machine learning techniques, early detection of diabetic eye disease using an automated system brings substantial benefits over manual detection. A variety of advanced studies relating to the detection of diabetic eye disease have recently been published. This article presents a systematic survey of automated approaches to diabetic eye disease detection from several aspects, namely: i) available datasets, ii) image preprocessing techniques, iii) deep learning models and iv) performance evaluation metrics. The survey provides a comprehensive synopsis of diabetic eye disease detection approaches, including state of the art field approaches, which aim to provide valuable insight into research communities, healthcare professionals and patients with diabetes.

Highlights

Diabetic Eye Disease (DED) comprises a group of eye conditions, which include Diabetic Retinopathy, Diabetic Macular Edema, Glaucoma and Cataract [1]
Afterwards, a three layer deep neural network was used to learn these features and subsequently, an SVM classifier was applied for the classification of DR fundus images into five severity stages, including no-DR, moderate, mild, severe nonproliferative diabetic retinopathy (NPDR) (Nonproliferative Diabetic Retinopathy) and proliferative diabetic retinopathy (PDR) (Proliferative Diabetic Retinopathy)
With three hidden layers, the deep features were extracted with Deep Belief Network (DBN), those features were decreased by applying the Generalised Regression Neural Network (GRNN) technique and the retinal images were classified using SVM

Summary

INTRODUCTION

Diabetic Eye Disease (DED) comprises a group of eye conditions, which include Diabetic Retinopathy, Diabetic Macular Edema, Glaucoma and Cataract [1]. Pressure can build up in the eyeball because the newly grown blood vessels interrupt the normal flow of the fluid This can damage the optic nerve that carries images from the eye to the brain, leading to glaucoma. Ting et al [11] published a review article focusing on eye conditions such as diabetic retinopathy, glaucoma, and age-related macular diseases They selected papers published between 2016 and 2018 and summarised them in their report. To address the limitations of the above-mentioned studies, this article offers a thorough analysis of both DL and TL approaches to automated DED detection published between 2014 and 2020 to cover the current DR detection methods built through DL or TL based approaches.

RESEARCH METHOD

IMAGE PREPROCESSING TECHNIQUES IN SELECTED ARTICLES

DIABETIC EYE DISEASE CLASSIFICATION TECHNIQUES

DISCUSSION AND OBSERVATIONS

Findings

VIII. CONCLUSION