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On the development of conjunctival hyperemia computer-assisted diagnosis tools: Influence of feature selection and class imbalance in automatic gradings.

, , , , , and . Artificial Intelligence in Medicine, (2016)

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Color Texture Analysis for Tear Film Classification: A Preliminary Study., , , , and . ICIAR (2), volume 6112 of Lecture Notes in Computer Science, page 388-397. Springer, (2010)iDEAS: A web-based system for dry eye assessment., , , , , , and . Computer Methods and Programs in Biomedicine, (2016)Automatic Selection of Video Frames for Hyperemia Grading., , , , and . EUROCAST, volume 9520 of Lecture Notes in Computer Science, page 479-486. Springer, (2015)On the analysis of feature selection techniques in a conjunctival hyperemia grading framework., , , , , and . ESANN, (2016)On the development of conjunctival hyperemia computer-assisted diagnosis tools: Influence of feature selection and class imbalance in automatic gradings., , , , , and . Artificial Intelligence in Medicine, (2016)Analysis of parameters for the automatic computation of the tear film break-up time test based on CCLRU standards., , , , , and . Computer Methods and Programs in Biomedicine, 113 (3): 715-724 (2014)Automatic classification of the interferential tear film lipid layer using colour texture analysis., , , , , and . Computer Methods and Programs in Biomedicine, 111 (1): 93-103 (2013)Tear Film Maps based on the Lipid Interference Patterns., , , and . ICAART (1), page 732-739. SciTePress, (2014)