Multiclass multilabel ophthalmological fundus image classification based on optimised deep feature space evolutionary model
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17530-z.pdf
Reference53 articles.
1. Bali A, Mansotra V (2021) Deep Learning-based Techniques for the Automatic Classification of Fundus Images : A Comparative Study. 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 351-359. https://doi.org/10.1109/ICAC3N53548.2021.9725464
2. Bali A, Mansotra V (2022) FUNDUS and OCT Image Classification Using DL Techniques. In: Rathore VS, Sharma SC, Tavares JMR, Moreira C, Surendiran B (eds) Rising Threats in Expert Applications and Solutions Lecture Notes in Networks and Systems, vol 434. Springer, Singapore. https://doi.org/10.1007/978-981-19-1122-4_8
3. Colucciello M (2004) Diabetic retinopathy. Control of systemic factors preserves vision. Postgrad Med 116(1):57–64. https://doi.org/10.3810/pgm.2004.07.1558
4. Bali, A, Mansotra, V (2023) Analysis of Deep Learning Techniques for Prediction of Eye Diseases: A Systematic Review. Arch Computat Methods Eng. https://doi.org/10.1007/s11831-023-09989-8
5. Dodo Y, Murakami T, Uji A, Yoshitake S, Yoshimura N (2015) Disorganized retinal lamellar structures in nonperfused areas of diabetic retinopathy. Investig Ophthalmol Visual Sci 56(3):2012–2020. https://doi.org/10.1167/iovs.14-15924
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