Hybrid Deep Learning Framework for Glaucoma Detection Using Fundus Images
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9521-9_14
Reference26 articles.
1. Sharmila C, Shanthi N (2021) Retinal image analysis for glaucoma detection using transfer learning. In: Advances in electrical and computer technologies. Springer, Singapore, pp 235–244. https://doi.org/10.1007/978-981-15-9019-1_21
2. Saxena A, Vyas A, Parashar L, Singh U (2020) A glaucoma detection using convolutional neural network. In: 2020 international conference on electronics and sustainable communication systems (ICESC), pp 815–820. https://doi.org/10.1109/ICESC48915.2020.9155930
3. Serener A, Serte S (2019) Transfer learning for early and advanced glaucoma detection with convolutional neural networks. In: 2019 medical technologies congress (TIPTEKNO), pp 1–4. https://doi.org/10.1109/TIPTEKNO.2019.8894965
4. Demir F, Taşcı B (2021) An effective and robust approach based on R-CNN+LSTM model and NCAR feature selection for ophthalmological disease detection from fundus images. J Personal Med 11(12):1276. https://doi.org/10.3390/jpm11121276
5. Olivas LG, Alférez GH, Castillo J (2021) Glaucoma detection in Latino population through OCT’s RNFL thickness map using transfer learning. Int Ophthalmol 41(11):1–15. https://doi.org/10.1007/s10792-021-01931-w
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