Deep Learning and Its Application in Diabetic Retinopathy Screening. Issue 6 (1st November 2020)

• Record Type: Journal Article
• Authors: Zou, Beiji; Shan, Xi; Zhu, Chengzhang; Dai, Yulan; Yue, Kejuan; Chen, Yuanqiong; Xiao, Yalong; Huang, Jiaer
• Is Part Of: Chinese journal of electronics. Volume 29:Issue 6(2020)
• Page Start: 992
• Page End: 1000
• Publication Date: 2020-11-01
• Subjects: biomedical optical imaging -- convolutional neural nets -- diseases -- eye -- learning (artificial intelligence) -- medical image processing -- reviews; complete blindness -- diabetic retinopathy screening systems -- deep learning -- automated retinal image analysis -- convolutional neural networks; Deep learning -- Convolutional neural networks -- Diabetic retinopathy screening -- Lesion detection; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2020.09.001
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : Deep learning (DL), especially Convolutional neural networks (CNN), has gained wide popularity in various image processing tasks. With the significant achievements obtained in DL, it has provided many successful solutions for real‐world applications as well as in medical domain. Automated retinal images analysis has been widely applied to screening Diabetic retinopathy (DR), which can greatly help preventing the occurrence of complete blindness when used in the early screening. In this paper, we mainly focus on DL, and we will give an overview of the deep learning‐based methods for DR screening. Finally, we will discuss the main issues encountered in the DR screening systems.