Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIEA.2012.6361007
Title: Pathological myopia detection from selective fundus image features
Authors: Zhang, Z.
Cheng, J.
Liu, J.
Sheri, Y.C.M.
Kong, C.C. 
Mei, S.S.
Keywords: Minimum Redundancy-Maximum Relevancy (mRMR)
Pathological Myopia
peripapillary atrophy (PPA)
Support Vector Machines (SVM)
Issue Date: 2012
Source: Zhang, Z.,Cheng, J.,Liu, J.,Sheri, Y.C.M.,Kong, C.C.,Mei, S.S. (2012). Pathological myopia detection from selective fundus image features. Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012 : 1742-1745. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIEA.2012.6361007
Abstract: We explore feature selection methodology for automatic Pathological Myopia detection via learning from an optimal set of features. An mRMR optimized classifier is trained using the candidate feature set to find the optimized classifier. We tested the proposed methodology on eye records of approximately 800 subjects collected from a population study. The experimental results demonstrate that the new classifier is much efficient by using less than 25% of the initial candidate feature set. The ranked optimal feature set also enables the clinicians to better access the diagnostic process of the algorithm. The work is a further step towards the advancement of the automatic pathological myopia diagnosis. © 2012 IEEE.
Source Title: Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012
URI: http://scholarbank.nus.edu.sg/handle/10635/73747
ISBN: 9781457721175
DOI: 10.1109/ICIEA.2012.6361007
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