Utilising Deep Learning Techniques, Detect and Categorise Diabetic Retinopathy

Author:

Sudhakar Reddy K.,Narayanan M.

Publisher

Springer Nature Singapore

Reference15 articles.

1. Carrera WV, Gonźalez A, Carrera R (2017) Automated detection of diabetic retinopathy using SVM. ISBN: 978-1-5090-6363-5/17/$31.00 ©2017 IEEE

2. Gandhi M, Dhanasekaran R (2013) Diagnosis of diabetic retinopathy using morphological process and SVM classifier. ISBN: 978-1-4673-4866-9/13/$31.00 ©2013 IEEE

3. Jahiruzzaman Md., Aowlad Hossain ABM (2015) Detection and classification of diabetic retinopathy using K-means clustering and fuzzy logic. ISBN: 978-1-4673-9930-2/15/$31.00 2015 IEEE

4. Manoj Kumar SB, Sheshadri HS (2016) Classification and detection of diabetic retinopathy using K-means algorithm. In: International conference on electrical, electronics, and optimization techniques (ICEEOT). ISBN: 978-1-4673-9939-5/16/$31.00 ©2016 IEEE

5. Bruno G, Cerquitelli T, Chiusano S, Xiao X (2014) A clustering-based approach to analyse examinations for diabetic patients. In: 2014 IEEE International Conference on healthcare informatics 2014. IEEE DOI https://doi.org/10.1109/ICHI.2014.14. ISBN: 978-1-4799-5701-9/14 $31.00 © 2014 IEEE

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