Glaucoma stage classification using UNET-based segmentation with multiple feature extraction technique
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-18243-7.pdf
Reference22 articles.
1. Sreng S, Maneerat N, Hamamoto K, Win KY (2020) Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images. Appl Sci 10(14):4916
2. Jiang Y, Duan L, Cheng J, Gu Z, Xia H, Fu H, Li C, Liu J (2019) JointRCNN: a region-based convolutional neural network for optic disc and cup segmentation. IEEE Trans Biomed Eng 67(2):335–343
3. Sevastopolsky A (2017) Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recognit Image Anal 27(3):618–624
4. Mohan D, Kumar JH, Seelamantula CS (2018) High-performance optic disc segmentation using convolutional neural networks. In: 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE, pp 4038–4042
5. Kumar E, Chigarapalle S (2021) Two-stage framework for optic disc segmentation and estimation of cup-todisc ratio using deep learning technique. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-02977-5
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