An ensemble approach for classification of diabetic retinopathy in fundus image-Reference-Cited by-同舟云学术

An ensemble approach for classification of diabetic retinopathy in fundus image

Published:2024-05-16 Issue: Volume: Page:
ISSN:1573-7721
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

J Prakash^ORCID,B Vinoth kumar

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11042-024-19353-y.pdf

Reference34 articles.

1. Hemalakshmi GR, Santhi D, Mani VRS et al (2021) Classification of retinal fundus image using MS-DRLBP features and CNN-RBF classifier. J Ambient Intell Human Comput 12:8747–8762. https://doi.org/10.1007/s12652-020-02647-y

2. Mahabadi N, Al Khalili Y (2024) Neuroanatomy, Retina. In: StatPearls. StatPearls Publishing, Treasure Island (FL). Available from: https://www.ncbi.nlm.nih.gov/books/NBK545310/. Accessed 8 Aug 2022

3. Khalifa NEM, Loey M, Taha MHN, Mohamed HNET (2019) Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection. Acta Inform Med 27(5):327–332. https://doi.org/10.5455/aim.2019.27.327-332

4. Bourne RR, Stevens GA, White RA, Smith JL, Flaxman SR, Price H, Jonas JB, Keeffe J, Leasher J, Naidoo K, Pesudovs K, Resnikoff S, Taylor HR (2013) Vision Loss Expert Group Causes of vision loss worldwide, 1990–2010: a systematic analysis. Lancet Glob Health 1(6):e339-49. https://doi.org/10.1016/S2214-109X(13)70113-X

5. Tufail A, Rudisill C, Egan C, Kapetanakis VV, Salas-Vega S, Owen CG, Lee A, Louw V, Anderson J, Liew G, Bolter L, Srinivas S, Nittala M, Sadda SriniVas, Taylor P, Rudnicka AR (2017) Automated Diabetic Retinopathy Image Assessment Software: Diagnostic Accuracy and Cost-Effectiveness Compared with Human Graders. Ophthalmology 124(3):343–351. https://doi.org/10.1016/j.ophtha.2016.11.014