Glaucoma Stage Classification Using Image Empirical Mode Decomposition (IEMD) and Deep Learning from Fundus Images
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48888-7_33
Reference17 articles.
1. Hagiwara, Y., et al.: Computer-aided diagnosis of glaucoma using fundus images: a review. Comput. Methods Programs Biomed. 165, 1–12 (2018)
2. Diaz-Pinto, A., Colomer, A., Naranjo, V., Morales, S., Xu, Y., Frangi, A.F.: Retinal image synthesis and semi-supervised learning for glaucoma assessment. IEEE Trans. Med. Imaging 38, 2211–2218 (2019). https://doi.org/10.1109/tmi.2019.2903434
3. Aloudat, M., Faezipour, M., El-Sayed, A.: High intraocular pressure detection from frontal eye images: a machine learning based approach. PubMed (2018). https://doi.org/10.1109/embc.2018.8513645
4. Saha, S.K., Fernando, B., Cuadros, J., Xiao, D., Kanagasingam, Y.: Automated quality assessment of colour fundus images for diabetic retinopathy screening in telemedicine. J. Digit. Imaging 31, 869–878 (2018). https://doi.org/10.1007/s10278-018-0084-9
5. Maheshwari, S., Pachori, R.B., Kanhangad, V., Bhandary, S.V., Acharya, U.R.: Iterative variational mode decomposition based automated detection of glaucoma using fundus images. Comput. Biol. Med. 88, 142–149 (2017). https://doi.org/10.1016/j.compbiomed.2017.06.017
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