Ocular disease detection from multiple informatics domains

Published

Conference Paper

© 2018 IEEE. Computer aided detection for automatic ocular disease detection is an important area of research. As different ocular diseases possess different characteristics and present at different locations within the eye, it is difficult to find a common way to effectively handle each ocular disease. To solve this problem, we propose a unified Multiple Kernel Learning framework called MKLclm to detect ocular diseases, based on the existence of multiple informatics domains. Our framework is capable to learn a robust predictive model by effectively integrating discriminative knowledge from different informatics domains and incorporating pre-learned Support Vector Machine (SVM) classifiers simultaneously. We validate MKLclm by conducting extensive experiments for three leading ocular diseases: glaucoma, age-related macular degeneration and pathological myopia. Experimental results show that MKLclm is significantly better than the standard SVMs using data from individual domains and the traditional MKL method.

Full Text

Duke Authors

Cited Authors

  • Xu, Y; Duan, L; Fu, H; Zhang, Z; Zhao, W; You, T; Wong, TY; Liu, J

Published Date

  • May 23, 2018

Published In

Volume / Issue

  • 2018-April /

Start / End Page

  • 43 - 47

Electronic International Standard Serial Number (EISSN)

  • 1945-8452

International Standard Serial Number (ISSN)

  • 1945-7928

International Standard Book Number 13 (ISBN-13)

  • 9781538636367

Digital Object Identifier (DOI)

  • 10.1109/ISBI.2018.8363519

Citation Source

  • Scopus