Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIP.2005.1529959
Title: Combining classifiers for bone fracture detection in x-ray images
Authors: Lum, V.L.F.
Leow, W.K. 
Chen, Y.
Howe, T.S.
Png, M.A.
Issue Date: 2005
Source: Lum, V.L.F.,Leow, W.K.,Chen, Y.,Howe, T.S.,Png, M.A. (2005). Combining classifiers for bone fracture detection in x-ray images. Proceedings - International Conference on Image Processing, ICIP 1 : 1149-1152. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIP.2005.1529959
Abstract: In medical applications, sensitivity in detecting medical problems and accuracy of detection are often in conflict. A single classifier usually cannot achieve both high sensitivity and accuracy at the same time. Methods of combining classifiers have been proposed in the literature. This paper presents a study of probabilistic combination methods applied to the detection of bone fractures in x-ray images. Test results show that the effectiveness of a method in improving both accuracy and sensitivity depends on the nature of the method as well as the proportion of positive samples. © 2005 IEEE.
Source Title: Proceedings - International Conference on Image Processing, ICIP
URI: http://scholarbank.nus.edu.sg/handle/10635/40916
ISBN: 0780391349
ISSN: 15224880
DOI: 10.1109/ICIP.2005.1529959
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