Survey on recent developments in automatic detection of diabetic retinopathy

被引:39
作者
Bilal, A. [1 ]
Sun, G. [1 ]
Mazhar, S. [1 ]
机构
[1] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
来源
JOURNAL FRANCAIS D OPHTALMOLOGIE | 2021年 / 44卷 / 03期
关键词
Ophthalmology; Diabetic retinopathy; Fundus images; Deep learning; Artificial intelligence; Machine learning; BLOOD-VESSEL SEGMENTATION; COLOR FUNDUS IMAGES; RETINAL IMAGES; OPTIC DISC; LESION DETECTION; GENERALIZED-METHOD; CLASSIFICATION; DIAGNOSIS; MICROANEURYSMS; SYSTEM;
D O I
10.1016/j.jfo.2020.08.009
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
Diabetic retinopathy (DR) is a disease facilitated by the rapid spread of diabetes worldwide. DR can blind diabetic individuals. Early detection of DR is essential to restoring vision and providing timely treatment. DR can be detected manually by an ophthalmologist, examining the retinal and fundus images to analyze the macula, morphological changes in blood vessels, hemorrhage, exudates, and/or microaneurysms. This is a time consuming, costly, and challenging task. An automated system can easily perform this function by using artificial intelligence, especially in screening for early DR. Recently, much state-of-the-art research relevant to the identification of DR has been reported. This article describes the current methods of detecting non-proliferative diabetic retinopathy, exudates, hemorrhage, and microaneurysms. In addition, the authors point out future directions in overcoming current challenges in the field of DR research. (C) 2021 Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:420 / 440
页数:21
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