Comparative Analysis of Deep Machine Learning Models for Identification of Glaucoma from Fundus Images
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2004-0_36
Reference24 articles.
1. Krishna N, Nagamani K (2023) Multi-modal imaging-based feature fusion for accurate glaucoma diagnosis with deep learning
2. Christopher M, Belghith A, Bowd C, Proudfoot JA, Goldbaum MH, Weinreb RN et al (2018) Performance of deep learning architectures and transfer learning for detecting glaucomatous optic neuropathy in fundus photographs. Sci Rep 8(1):16685
3. Ahmad H, Yamin A, Shakeel A, Gillani SO, Ansari U (2014) Detection of glaucoma using retinal fundus images. In: 2014 International conference on robotics and emerging allied technologies in engineering (iCREATE). IEEE, pp 321–324
4. Huang X, Kong X, Shen Z, Ouyang J, Li Y, Jin K, Ye J (2023) GRAPE: a multi-modal dataset of longitudinal follow-up visual field and fundus images for glaucoma management. Sci Data 10(1):520
5. Serener A, Serte S (2019) Transfer learning for early and advanced glaucoma detection with convolutional neural networks. In: 2019 Medical Technologies Congress (TIPTEKNO). IEEE, pp 1–4
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