Classification of diabetic retinopathy using neural networks

被引:0
作者
Nguyen, HT [1 ]
Butler, M [1 ]
Roychoudhry, A [1 ]
Shannon, AG [1 ]
Flack, J [1 ]
Mitchell, P [1 ]
机构
[1] Univ Technol Sydney, Sydney, NSW 2007, Australia
来源
PROCEEDINGS OF THE 18TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOL 18, PTS 1-5 | 1997年 / 18卷
关键词
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Classification of the severity of diabetic retinopathy (DR) and quantification of diabetic changes are viral for assessing the therapies and risk factors for this frequent complication of diabetes. A multilayer feedforward network has been developed for the classification of DR. One of its major strengths is that accurate feature extractions and accurate grading of DR lesions are not required. Another strength of this technique is its robustness as the network can also classify DR effectively in noisy environments.
引用
收藏
页码:1548 / 1549
页数:2
相关论文
共 50 条
[41]   Classification of Diabetic Retinopathy using Statistical Region Merging and Convolutional Neural Network [J].
Wulandari, Chintya Dewi Regina ;
Wibowo, Suryo Adhi ;
Novamizanti, Ledya .
2019 IEEE ASIA PACIFIC CONFERENCE ON WIRELESS AND MOBILE (APWIMOB), 2019, :94-98
[42]   Convolutional Neural Network for Classification of Diabetic Retinopathy Grade [J].
Alcala-Rmz, Vanessa ;
Maeda-Gutierrez, Valeria ;
Zanella-Calzada, Laura A. ;
Valladares-Salgado, Adan ;
Celaya-Padilla, Jose M. ;
Galvan-Tejada, Carlos E. .
ADVANCES IN SOFT COMPUTING, MICAI 2020, PT I, 2020, 12468 :104-118
[43]   Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks [J].
Sundaram, Swaminathan ;
Selvamani, Meganathan ;
Raju, Sekar Kidambi ;
Ramaswamy, Seethalakshmi ;
Islam, Saiful ;
Cha, Jae-Hyuk ;
Almujally, Nouf Abdullah ;
Elaraby, Ahmed .
DIAGNOSTICS, 2023, 13 (05)
[44]   Grading Diabetic Retinopathy Severity Using Modern Convolution Neural Networks (CNN) [J].
Lee, Andrew ;
Khushi, Matloob ;
Hao, Patrick ;
Uddin, Shahadat ;
Poon, Simon K. .
2021 IEEE INTERNATIONAL CONFERENCE ON DIGITAL HEALTH (ICDH 2021), 2021, :19-26
[45]   Automated detection of diabetic retinopathy using convolutional neural networks on a small dataset [J].
Samanta, Abhishek ;
Saha, Aheli ;
Satapathy, Suresh Chandra ;
Fernandes, Steven Lawrence ;
Zhang, Yu-Dong .
PATTERN RECOGNITION LETTERS, 2020, 135 :293-298
[46]   Diabetic retinopathy detection using red lesion localization and convolutional neural networks [J].
Zago, Gabriel Tozatto ;
Andreao, Rodrigo Varejao ;
Dorizzi, Bernadette ;
Teatini Salles, Evandro Ottoni .
COMPUTERS IN BIOLOGY AND MEDICINE, 2020, 116
[47]   Improving the Curvelet Saliency and Deep Convolutional Neural Networks for Diabetic Retinopathy Classification in Fundus Images [J].
Vo Thi Hong Tuyet ;
Nguyen Thanh Binh ;
Tin, Dang Thanh .
ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, 2022, 12 (01) :8204-8209
[48]   Automated identification and grading system of diabetic retinopathy using deep neural networks [J].
Zhang, Wei ;
Zhong, Jie ;
Yang, Shijun ;
Gao, Zhentao ;
Hu, Junjie ;
Chen, Yuanyuan ;
Yi, Zhang .
KNOWLEDGE-BASED SYSTEMS, 2019, 175 :12-25
[49]   DETECTION OF DIABETIC-RETINOPATHY USING NEURAL NETWORKS ANALYSIS OF FUNDUS IMAGES [J].
GARDNER, GG ;
KEATING, D ;
WILLIAMSON, TH ;
ELLIOTT, AT .
VISION RESEARCH, 1995, 35 :P331-P331
[50]   Deep and Densely Connected Networks for Classification of Diabetic Retinopathy [J].
Riaz, Hamza ;
Park, Jisu ;
Choi, Hojong ;
Kim, Hyunchul ;
Kim, Jungsuk .
DIAGNOSTICS, 2020, 10 (01)