Transforming Retinal Photographs to Entropy Images in Deep Learning to Improve Automated Detection for Diabetic Retinopathy.

Gen-Min Lin,Mei-Juan Chen,Ying Gao,Ming-Chin Chen,Yu-Yang Lin,Min-Hui Lin,Heng-Yu Kuo,Anran Ran,Shinfeng D Lin,Carol Y Cheung,Chia-Hung Yeh

doi:10.1155/2018/2159702

Abstract

Entropy images, representing the complexity of original fundus photographs, may strengthen the contrast between diabetic retinopathy (DR) lesions and unaffected areas. The aim of this study is to compare the detection performance for severe DR between original fundus photographs and entropy images by deep learning. A sample of 21,123 interpretable fundus photographs obtained from a publicly available data set was expanded to 33,000 images by rotating and flipping. All photographs were transformed into entropy images using block size 9 and downsized to a standard resolution of 100 × 100 pixels. The stages of DR are classified into 5 grades based on the International Clinical Diabetic Retinopathy Disease Severity Scale: Grade 0 (no DR), Grade 1 (mild nonproliferative DR), Grade 2 (moderate nonproliferative DR), Grade 3 (severe nonproliferative DR), and Grade 4 (proliferative DR). Of these 33,000 photographs, 30,000 images were randomly selected as the training set, and the remaining 3,000 images were used as the testing set. Both the original fundus photographs and the entropy images were used as the inputs of convolutional neural network (CNN), and the results of detecting referable DR (Grades 2–4) as the outputs from the two data sets were compared. The detection accuracy, sensitivity, and specificity of using the original fundus photographs data set were 81.80%, 68.36%, 89.87%, respectively, for the entropy images data set, and the figures significantly increased to 86.10%, 73.24%, and 93.81%, respectively (all p values <0.001). The entropy image quantifies the amount of information in the fundus photograph and efficiently accelerates the generating of feature maps in the CNN. The research results draw the conclusion that transformed entropy imaging of fundus photographs can increase the machinery detection accuracy, sensitivity, and specificity of referable DR for the deep learning-based system.

Highlights

Diabetic retinopathy (DR) is one of the microvascular complications related to diabetes mellitus and a major cause of blindness globally
Deep learning is a subset of machine learning, and in modern medicine, using deep learning in fundus photography has emerged as a cost-effective and practical method for automated grading of DR [5, 7, 8]
Our study demonstrates that, compared with the original fundus photographs, the entropy images can improve the deep learning performance and correctly detect the cliniciandefined referable DR

Summary

Introduction

Diabetic retinopathy (DR) is one of the microvascular complications related to diabetes mellitus and a major cause of blindness globally. In the United States, the DR prevalence among diabetic patients is between 20% and 30% [1, 2]. Fundus photography is a direct visual screening tool used to detect DR and has been widely accepted worldwide. The detection of DR and assessment of its severity require specialized expertise, and the agreement of interpretation results between examiners varied substantially based on previous studies [3,4,5]. Journal of Ophthalmology patients do not have access to effective screening programs and some cannot afford the cost of an ophthalmologist visit [6]. Deep learning is a subset of machine learning, and in modern medicine, using deep learning in fundus photography has emerged as a cost-effective and practical method for automated grading of DR [5, 7, 8]

Methods

Results

Conclusion