Detection and classification of red lesions from retinal images for diabetic retinopathy detection using deep learning models-Reference-Cited by-同舟云学术

Detection and classification of red lesions from retinal images for diabetic retinopathy detection using deep learning models

Published:2023-03-27 Issue:25 Volume:82 Page:39327-39347
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Saranya P,Pranati R,Patro Sneha Shruti

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/s11042-023-15045-1.pdf

Reference37 articles.

1. Agarwal S, Bhat A (2022) A survey on recent developments in diabetic retinopathy detection through integration of deep learning. Multimed Tools Appl:1–31. https://doi.org/10.1007/s11042-022-13837-5

2. Antal B, Hajdu A (2012) An ensemble-based system for microaneurysm detection and diabetic retinopathy grading. IEEE Trans Biomed Eng 59(6):1720–1726

3. Brownlee J (2019) How to accelerate learning of deep neural networks with batch normalization. Retrieved from Machine Learning Mastry: https://machinelearningmastery.com/how-to-accelerate-learning-of-deep-neural-networks-with-batch-normalization/

4. Chetoui M, Akhloufi MA (2020) Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets. J Med Imaging (Bellingham) 7(4):044503

5. Choong KH, Basah SN, Yazid H, Safar MJA, Ahmad Saad FS, Lim CC (2022) Performance analysis of multi-level thresholding for microaneurysm detection. Multimed Tools Appl 81:31161–31180

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