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|Title:||Combining classifiers for bone fracture detection in x-ray images||Authors:||Lum, V.L.F.
|Issue Date:||2005||Citation:||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|
|Appears in Collections:||Staff Publications|
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