Please use this identifier to cite or link to this item:
|Title:||MRMR optimized classification for automatic glaucoma diagnosis|
|Source:||Zhang, Z.,Kwoh, C.K.,Liu, J.,Yin, F.,Wirawan, A.,Cheung, C.,Baskaran, M.,Aung, T.,Wong, T.Y. (2011). MRMR optimized classification for automatic glaucoma diagnosis. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS : 6228-6231. ScholarBank@NUS Repository. https://doi.org/10.1109/IEMBS.2011.6091538|
|Abstract:||Min-Redundancy Max-Relevance (mRMR) is a feature selection methodology based on information theory. We explore the mRMR principle for automatic glaucoma diagnosis. Optimal candidate feature sets are acquired from a composition of clinical screening data and retinal fundus image data. An mRMR optimized classifier is further trained using the candidate feature sets to find the optimized classifier. We tested the proposed methodology on eye records of 650 subjects collected from Singapore Eye Research Institute. The experimental results demonstrate that the new classifier is much compact by using less than of the initial feature set. The ranked 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 glaucoma diagnosis. © 2011 IEEE.|
|Source Title:||Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 12, 2017
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.