Open AccessMachine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images
Author(s) -
Yu Wang,
Yaonan Zhang,
Zhaomin Yao,
Ruxing Zhao,
Fengfeng Zhou
Publication year - 2016
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.7.004928
Subject(s) - optical coherence tomography , macular degeneration , diabetic macular edema , medicine , ophthalmology , macular edema , artificial intelligence , diabetic retinopathy , computer science , feature selection , optometry , computer vision , visual acuity , diabetes mellitus , endocrinology
Non-lethal macular diseases greatly impact patients' life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples.