A meta-analysis on diabetic retinopathy and deep learning applications
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/s11042-023-17784-7.pdf
Reference132 articles.
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2. Bell N, Garland M (2018) Efficient Sparse Matrix-Vector Multiplication on CUDA, NVIDIA Technical Report
3. Asiri N, Hussain M, Al Adel F, Alzaidi N (2019) Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey. Artif Intell Med 99:101701. https://doi.org/10.1016/j.artmed.2019.07.009
4. Alyoubi WL, Shalash WM, Abulkhair MF (2020) Diabetic retinopathy detection through deep learning techniques: A review. Informatics in Medicine Unlocked 20:100377. https://doi.org/10.1016/j.imu.2020.100377
5. Salamat N, Missen MMS, Rashid A (2019) Diabetic retinopathy techniques in retinal images: a review. Artif Intell Med 97:168–188. https://doi.org/10.1016/j.artmed.2018.10.009
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Exploring Machine Learning Models for Predicting Diabetic Retinopathy: A Comprehensive Comparative Study of Logistic Regression an Advanced Technique;International Journal of Innovative Science and Research Technology (IJISRT);2024-08-07
2. Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas;International Journal of Computational Intelligence Systems;2024-05-29
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