Deep Learning Glaucoma Detection Models in Retinal Images Capture by Mobile Devices
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-32029-3_1
Reference24 articles.
1. Claro, M.L., Veras, R., Santos, L., Frazão, M., Carvalho Filho, A., Leite, D.: Métodos computacionais para segmentação do disco óptico em imagens de retina: uma revisão. Rev. Bras. Comput. Apl. 10(2), 29–43 (2018). https://doi.org/10.5335/rbca.v10i2.7661
2. Bajwa, M.N., et al.: Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC Med. Inform. Decis. Mak. 19(1), 136 (2019). https://doi.org/10.1186/s12911-019-0842-8
3. Stella Mary, M.C.V., Rajsingh, E.B., Naik, G.R.: Retinal fundus image analysis for diagnosis of glaucoma: a comprehensive survey. IEEE Access 4, 4327–4354 (2016). https://doi.org/10.1109/ACCESS.2016.2596761
4. Hagiwara, Y., et al.: Computer-aided diagnosis of glaucoma using fundus images: a review. Comput. Methods Programs Biomed. 165, 1–12 (2018). https://doi.org/10.1016/j.cmpb.2018.07.012
5. Camara, J., Neto, A., Pires, I.M., Villasana, M.V., Zdravevski, E., Cunha, A.: A comprehensive review of methods and equipment for aiding automatic glaucoma tracking. Diagnostics 12(4), 935 (2022). https://doi.org/10.3390/diagnostics12040935
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