An Enhanced RNN-LSTM Model for Fundus Image Classification to Diagnose Glaucoma
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42979-024-02867-5.pdf
Reference54 articles.
1. Weinreb RN, Bowd C, Moghimi S, Tafreshi A, Rausch S, Zangwill LM. Ophthalmic diagnostic imaging: glaucoma. In: High resolution imaging in microscopy and ophthalmology. Cham: Springer; 2019. p. 107–34. https://doi.org/10.1007/978-3-030-16638-05.
2. Li Z, Jiang J, Zhou H, Zheng Q, Liu X, Chen K, Weng H, Chen W. Development of a deep learning-based image eligibility verification system for detecting and filtering out ineligible fundus images: a multicentre study. Int J Med Inform. 2021;147: 104363. https://doi.org/10.1016/j.ijmedinf.2020.104363.
3. Septiarini A, Khairina DM, Kridalaksana AH, Hamdani H. Automatic glaucoma detection method applying a statistical approach to fundus images. Healthc Inform Res. 2018;24:53. https://doi.org/10.4258/hir.2018.24.1.53.
4. Zilly J, Buhmann JM, Mahapatra D. Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput Med Imaging Graph. 2017;55:28–41. https://doi.org/10.1016/j.compmedimag.2016.07.012.
5. Pathan S, Kumar P, Pai RM, Bhandary SV. Automated segmentation and classification of retinal features for glaucoma diagnosis. Biomed Signal Process Control. 2021;63: 102244. https://doi.org/10.1016/j.bspc.2020.102244.
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