OphthaPredict: Automatic Classification of Conjunctivitis Using Deep Learning Architecture
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9524-0_35
Reference21 articles.
1. Van Weert HC, Tellegen E, Ter Riet G (2014) A new diagnostic index for bacterial conjunctivitis in primary care. A re-derivation study. Eur J Gen Pract 20(3):202–208. https://doi.org/10.3109/13814788.2013.842970
2. Yeu E, Hauswirth S (2020) A review of the differential diagnosis of acute infectious conjunctivitis: implications for treatment and management. Clin Ophthalmol (Auckland, N.Z.) 14:805–813. https://doi.org/10.2147/OPTH.S236571
3. Rietveld RP, van Weert HC, ter Riet G, Bindels PJ (2003) Diagnostic impact of signs and symptoms in acute infectious conjunctivitis: systematic literature search. BMJ (Clin Res ed.) 327(7418):789. https://doi.org/10.1136/bmj.327.7418.789
4. Azari AA, Arabi A (2020) Conjunctivitis: a systematic review. J Ophthalmic Vis Res 15(3):372–379. https://doi.org/10.18502/jovr.v15i3.7456
5. Udeh BL, Schneider JE, Ohsfeldt RL (2008) Cost effectiveness of a point-of-care test for adenoviral conjunctivitis. Am J Med Sci 336(3):254–264. https://doi.org/10.1097/MAJ.0b013e3181637417
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