Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40465
Title: Hierarchical classifiers for detection of fractures in X-ray images
Authors: He, J.C.
Leow, W.K. 
Howe, T.S.
Issue Date: 2007
Source: He, J.C.,Leow, W.K.,Howe, T.S. (2007). Hierarchical classifiers for detection of fractures in X-ray images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4673 LNCS : 962-969. ScholarBank@NUS Repository.
Abstract: Fracture of the bone is a very serious medical condition. In clinical practice, a tired radiologist has been found to miss fracture cases after looking through many images containing healthy bones. Computer detection of fractures can assist the doctors by flagging suspicious cases for closer examinations and thus improve the timeliness and accuracy of their diagnosis. This paper presents a new divide-and-conquer approach for fracture detection by partitioning the problem into smaller sub-problems in SVM's kernel space, and training an SVM to specialize in solving each sub-problem. As the sub-problems are easier to solve than the whole problem, a hierarchy of SVMs performs better than an individual SVM that solves the whole problem. Compared to existing methods, this approach enhances the accuracy and reliability of SVMs. © Springer-Verlag Berlin Heidelberg 2007.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/40465
ISBN: 9783540742715
ISSN: 03029743
Appears in Collections:Staff Publications

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