Classification of retinal fundus image using MS-DRLBP features and CNN-RBF classifier-Reference-Cited by-同舟云学术

Classification of retinal fundus image using MS-DRLBP features and CNN-RBF classifier

Published:2020-11-07 Issue:9 Volume:12 Page:8747-8762
ISSN:1868-5137
Container-title:Journal of Ambient Intelligence and Humanized Computing
language:en
Short-container-title:J Ambient Intell Human Comput

Author:

Hemalakshmi G. R.^ORCID,Santhi D.,Mani V. R. S.,Geetha A.,Prakash N. B.

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s12652-020-02647-y.pdf

Reference69 articles.

1. Department of Economic and Social Affairs (2017) World population prospects the 2017 revision key findings and advance tables, United Nations, New York

2. Prince MJ et al (2015) The burden of disease in older people and implications for health policy and practice. Lancet 385(9967):549–562

3. A Damayanti (2017) Fuzzy learning vector quantization, neural network and fuzzy systems for classification fundus eye images with wavelet transformation. In: 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp 331–336. IEEE

4. Y Yan, D Wen, M Ali Akber Dewan, W-B Huang (2017) Classification of artery and vein in retinal fundus images based on the context-dependent features. In: International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, pp 198–213. Springer, Cham

5. Z Qiao, Q Zhang, Y Dong, J-J Yang (2017) Application of SVM based on genetic algorithm in classification of cataract fundus images. In: 2017 IEEE International Conference on Imaging Systems and Techniques (IST), pp 1–5. IEEE

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