A Comprehensive Review of Diabetic Retinopathy Detection and Grading Based on Deep Learning and Metaheuristic Optimization Techniques
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11831-023-09946-5.pdf
Reference157 articles.
1. Xiao D, Bhuiyan A, Frost S et al (2019) Major automatic diabetic retinopathy screening systems and related core algorithms : a review. Mach Vis Appl 30:423–446. https://doi.org/10.1007/s00138-018-00998-3
2. IDF Diabetes Atlas (2021) Tenth Edition. In: International Diabetes Federation. https://diabetesatlas.org/. Accessed 13 Dec 2021
3. Bhandari S, Pathak S, Amit S (2022) A literature review of early—stage diabetic retinopathy detection using deep learning and evolutionary computing techniques. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-022-09816-6
4. Tsiknakis N, Theodoropoulos D, Manikis G et al (2021) Deep learning for diabetic retinopathy detection and classification based on fundus images : a review. Comput Biol Med 135:104599. https://doi.org/10.1016/j.compbiomed.2021.104599
5. Dayana AM, Emmanuel WRS (2022) An enhanced swarm optimization-based deep neural network for diabetic retinopathy classification in fundus images. Multimed Tools Appl 81(15):20611–20641
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