Revolutionizing Glaucoma Diagnosis with a Hybrid AI Algorithm
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-64070-4_5
Reference17 articles.
1. Al-Bander, B., Al-Nuaimy, W., Al-Taee, M.A., Zheng, Y.: Automated glaucoma diagnosis using deep learning approach. In: 2017 14th International Multi-Conference on Systems, Signals Devices (SSD), pp. 207–210. IEEE (2017)
2. Chai, Y., Liu, H., Xu, J.: Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models. Knowl.-Based Syst. 161, 147–156 (2018)
3. Akter, N., Fletcher, J., Perry, S., Simunovic, M.P., Briggs, N., Roy, M.: Glaucoma diagnosis using multi-feature analysis and a deep learning technique. Sci. Rep. 12(1), 8064 (2022)
4. Kashyap, R., Nair, R., Gangadharan, S.M.P., Botto-Tobar, M., Farooq, S., Rizwan, A.: Glaucoma detection and classification using improved U-Net Deep Learning Model. In: Healthcare, vol. 10, no. 12, p. 2497. MDPI (2022)
5. An, G., et al.: Glaucoma diagnosis with machine learning based on optical coherence tomography and color fundus images. J. Healthc. Eng. 2019, 1–9 (2019)
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