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Abstract #60223 Published in IGR 16-4
Cross-examination for Angle-Closure Glaucoma Feature Detection
S Issac N; Lin W; Kwoh CK; Kuo J; Aquino MC; Sng C; Chew PTIEEE journal of biomedical and health informatics 2016; 20: 343-354
Effective feature selection plays a vital role in anterior segment imaging for determining the mechanism involved in angle-closure glaucoma (ACG) diagnosis. This research focuses on the use of redundant features for complex disease diagnosis such as ACG using anterior segment optical coherence tomography (AS-OCT) images. Both supervised (Minimum Redundancy Maximum Relevance (MRMR)) and unsupervised (Laplacian Score (L-score)) feature selection algorithms have been cross-examined with different ACG mechanisms. An AdaBoost machine learning classifier is then used for classifying the five various classes of ACG mechanism such as Iris Roll, Lens, Pupil Block, Plateau Iris and No Mechanism using both feature selection methods. The overall accuracy has shown that the usefulness of redundant features by L-Score method in improved ACG diagnosis compared to minimum redundant features by MRMR method.
Full article
Classification:
9.3.5 Primary angle closure (Part of: 9 Clinical forms of glaucomas > 9.3 Primary angle closure glaucomas)
