An Effective Diabetic Retinopathy Detection Using Hybrid Convolutional Neural Network Models
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-20541-5_14
Reference17 articles.
1. Bhatia, K., Arora, S., & Tomar, R. (2016). Diagnosis of diabetic retinopathy using machine learning classification algorithm. In 2016 2nd International Conference on Next Generation Computing Technologies (NGCT) (pp. 347–351). https://doi.org/10.1109/NGCT.2016.7877439
2. Boral, Y. S., & Thorat, S. S. (2021). Classification of diabetic retinopathy based on hybrid neural network. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1354–1358). https://doi.org/10.1109/ICCMC51019.2021.9418224
3. Carrera, E. V., González, A., & Carrera, R. (2017). Automated detection of diabetic retinopathy using SVM. In 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON) (pp. 1–4). https://doi.org/10.1109/INTERCON.2017.8079692
4. Cuadros, J., Bresnick, G. (2009). EyePACS: an adaptable telemedicine system for diabetic retinopathy screening. Journal of Diabetes Science and Technology, 3, 509–516.
5. Harun, N. H., Yusof, Y., Hassan, F., & Embong, Z. (2019). Classification of fundus images for diabetic retinopathy using artificial neural network. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 498–501). https://doi.org/10.1109/JEEIT.2019.8717479
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