Multistage Classification of Retinal Images for Prediction of Diabetic Retinopathy-Based Deep Learning Model
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-1946-8_20
Reference20 articles.
1. Satish U, Abbddularafath SK, Rishindra V, Mukhesh Y, Sai Harsha V (2020 May) Detection of diabetic retinopathy using CNN, © MAY 2020 IRE Journals 3:11 ISSN: 2456–8880
2. Mohammad Z Atwany, Abdulwahab H Sahyoun, Mohammad Yaqub, Deep learning techniques for diabetic retinopathy classification: a survey, digital object identifier https://doi.org/10.1109/ACCESS.2022.3157632, IEEE2022.
3. Voets M, Møllersen K, Bongo LA (2019) Reproduction study using public data of Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. PLoS ONE 14(6):1–11. https://doi.org/10.1371/journal.pone.0217541
4. Nitigya Sambyal, Poonam Saini, Rupali Syal, Varun Gupta (2020) Modified U-Net architecture for semantic segmentation of diabetic retinopathy images. Biocybern Biomed Eng, S0208521620300747. https://doi.org/10.1016/j.bbe.2020.05.006
5. Andrzej Grzybowski, Piotr Brona1, Gilbert Lim, Paisan Ruamviboonsuk, Gavin SW Tan, Michael Abramoff, Daniel SW Ting (2020) Artificial intelligence for diabetic retinopathy screening: a review. Eye 34:451–460. https://doi.org/10.1038/s41433-019-0566
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