@inproceedings{16fc5f5ba2af4de496ef377dd2639d95,
title = "Intelligent fusion of cup-to-disc ratio determination methods for glaucoma detection in ARGALI",
abstract = "Glaucoma is a leading cause of permanent blindness. ARGALI, an automated system for glaucoma detection, employs several methods for segmenting the optic cup and disc from retinal images, combined using a fusion network, to determine the cup to disc ratio (CDR), an important clinical indicator of glaucoma. This paper discusses the use of SVM as an alternative fusion strategy in ARGALI, and evaluates its performance against the component methods and neural network (NN) fusion in the CDR calculation. The results show SVM and NN provide similar improvements over the component methods, but with SVM having a greater consistency over the NN, suggesting potential for SVM as a viable option in ARGALI.",
author = "Wong, {D. W.K.} and J. Liu and Lim, {J. H.} and Tan, {N. M.} and Z. Zhang and S. Lu and H. Li and Teo, {M. H.} and Chan, {K. L.} and Wong, {T. Y.}",
note = "Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 ; Conference date: 02-09-2009 Through 06-09-2009",
year = "2009",
doi = "10.1109/IEMBS.2009.5332534",
language = "English",
isbn = "9781424432967",
series = "Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009",
publisher = "IEEE Computer Society",
pages = "5777--5780",
booktitle = "Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society",
address = "United States",
}