A study on effective data preprocessing and augmentation method in diabetic retinopathy classification using pre-trained deep learning approaches
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/s11042-023-15754-7.pdf
Reference55 articles.
1. Abbood SH, Hamed HNA, Rahim MSM, Rehman A, Saba T, Bahaj SA (2022) Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model. IEEE 73079–73086. https://doi.org/10.1109/ACCESS.2022.3189374
2. AĞCA K, TAKCI H (2022) Hibrit Bir Model Oluşturarak Diyabetik Retinopati Sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi (36):227-236. https://doi.org/10.31590/ejosat.1112980
3. Al-Antary MT, Arafa Y (2021) Multi-scale attention network for diabetic retinopathy classification. IEEE Access 9:54190–54200. https://doi.org/10.1109/ACCESS.2021.3070685
4. APTOS Blindness Detection (2019) https://www.kaggle.com/competitions/aptos2019-blindness-detection/data. Accessed 12 Feb 2023
5. Berbar MA (2022) Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy. Health Information Science and Systems 10(1):1–13. https://doi.org/10.1007/s13755-022-00181-z
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