Volume 29 Issue 6
Dec.  2020
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ZOU Beiji, SHAN Xi, ZHU Chengzhang, DAI Yulan, YUE Kejuan, CHEN Yuanqiong, XIAO Yalong, HUANG Jiaer. Deep Learning and Its Application in Diabetic Retinopathy Screening[J]. Chinese Journal of Electronics, 2020, 29(6): 992-1000. doi: 10.1049/cje.2020.09.001
Citation: ZOU Beiji, SHAN Xi, ZHU Chengzhang, DAI Yulan, YUE Kejuan, CHEN Yuanqiong, XIAO Yalong, HUANG Jiaer. Deep Learning and Its Application in Diabetic Retinopathy Screening[J]. Chinese Journal of Electronics, 2020, 29(6): 992-1000. doi: 10.1049/cje.2020.09.001

Deep Learning and Its Application in Diabetic Retinopathy Screening

doi: 10.1049/cje.2020.09.001
Funds:  This work is supported by the National Natural Science Foundation of China (No.61702559, No.61902434), National Science and Technology Major Project (No.2018AAA0102102), the Planned Science and Technology Project of Hunan Province, China (No.2017WK2074), the Natural Science Foundation of Hunan Province, China (No.2018JJ3686, No.2019JJ50826), and the 111 Project (No.B18059).
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  • Corresponding author: ZHU Chengzhang (corresponding author) received the Ph.D. degree from School of Information Science and Engineering, Central South University, Changsha, China, in 2016. She is currently a associate professor at Central South University, Changsha, China. Her research interests include medical image processing, computer vision and pattern recognition. (Email:anandawork@126.com)
  • Received Date: 2019-10-31
  • Publish Date: 2020-12-25
  • 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.
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