Machine learning for glaucoma detection using fundus images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42600-023-00305-8.pdf
Reference44 articles.
1. Elmoufidi A, Skouta A, Jai-Andaloussi S, Ouchetto O. Cnn with multiple inputs for automatic glaucoma assessment using fundus images. Int J Image Graph. 2023;23(01):2350012.
2. Elmoufidi A, Skouta A, Jai-andaloussi S, Ouchetto O. Deep multiple instance learning for automatic glaucoma prevention and autoannotation using color fundus photography. Prog Artif Intell. 2022;11(4):397–409.
3. Almazroa A, Burman R, Raahemifar K, Lakshminarayanan V. Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey. Journal of ophthalmology.2015;2015.
4. Tsai T, Reinehr S, Maliha AM, Joachim SC. Immune mediated degeneration and possible protection in glaucoma. Frontiers in neuroscience.2019;13:931.
5. Elmoufidi A, Fahssi KE, Jai-Andaloussi S, Madrane N, Sekkaki A. Detection of regions of interest’s in mammograms by using local binary pattern, dynamic k-means algorithm and gray level cooccurrence matrix. In 2014 International Conference on Next Generation Networks and Services (NGNS), pages 118–123. IEEE, 2014.
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