Detection and evaluation of Keratoconus (corneal topography) by using the image classifier techniques
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00500-020-05255-2.pdf
Reference24 articles.
1. Accardo PA, Pensiero S (2002) Neural network-based system for early Keratoconus detection from corneal topography. J Biomed Inform 35(3):151–159
2. Alio JL, Abbouda A, Valle DD, Del Castillo JMB, Fernandez JAG (2013) Corneal cross linking and infectious keratitis: a systematic review with a meta-analysis of reported cases. J Ophthalmic Inflamm Infect 3:47
3. Anandakumar H, Umamaheswari K (2017) Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Cluster Comput 20(2):1505–1515
4. Asher R, Gefen A, Moisseiev E, Varssano D (2014) Etiology of Keratoconus: proposed biomechanical pathogenesis. Silico Cell Tissue Sci 1:3
5. Dhaini AR, Chokr M, Maria el-oud S, Fattah MA, Awwad S (2017) Automated detection and measurement of corneal haze and demarcation line in spectral domain optical coherence tomography images digital object identifier. https://doi.org/10.1109/access.2018.2789526
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