Automated method for real-time AMD screening of fundus images dedicated for mobile devices
Author:
Funder
Campus France
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11517-022-02546-8.pdf
Reference89 articles.
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2. Mookiah MRK, Acharya UR, Fujita H, Koh JEW, Tan JH, Noronha K et al (2015) Local configuration pattern features for age-related macular degeneration characterization and classification. Computers in Biology and Medicine 63:208–218. https://doi.org/10.1016/j.compbiomed.2015.05.019
3. Wong WL, Su X, Li X, Cheung CMG, Klein R, Cheng C-Y, Wong TY (2014) Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. The Lancet Global Health 2(2):e106–e116. https://doi.org/10.1016/S2214-109X(13)70145-1
4. Köse C, Şevik U, Gençalioğlu O, İkibaş C, Kayıkıçıoğlu T (2010) A statistical segmentation method for measuring age-related macular degeneration in retinal fundus images. Journal of Medical Systems 34(1):1–13. https://doi.org/10.1007/s10916-008-9210-4
5. Pead E, Megaw R, Cameron J, Fleming A, Dhillon B, Trucco E, MacGillivray T (2019) Automated detection of age-related macular degeneration in color fundus photography: a systematic review. Survey of Ophthalmology 64(4):498–511. https://doi.org/10.1016/j.survophthal.2019.02.003
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