The effect of feature selection methods on computer-aided detection of masses in mammograms.
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Publication year
2010Source
Physics in Medicine and Biology, 55, 10, (2010), pp. 2893-904ISSN
Publication type
Article / Letter to editor
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Organization
Radiology
Journal title
Physics in Medicine and Biology
Volume
vol. 55
Issue
iss. 10
Page start
p. 2893
Page end
p. 904
Subject
ONCOL 5: Aetiology, screening and detection; Medical Imaging - Radboud University Medical CenterAbstract
In computer-aided diagnosis (CAD) research, feature selection methods are often used to improve generalization performance of classifiers and shorten computation times. In an application that detects malignant masses in mammograms, we investigated the effect of using a selection criterion that is similar to the final performance measure we are optimizing, namely the mean sensitivity of the system in a predefined range of the free-response receiver operating characteristics (FROC). To obtain the generalization performance of the selected feature subsets, a cross validation procedure was performed on a dataset containing 351 abnormal and 7879 normal regions, each region providing a set of 71 mass features. The same number of noise features, not containing any information, were added to investigate the ability of the feature selection algorithms to distinguish between useful and non-useful features. It was found that significantly higher performances were obtained using feature sets selected by the general test statistic Wilks' lambda than using feature sets selected by the more specific FROC measure. Feature selection leads to better performance when compared to a system in which all features were used.
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- Academic publications [245050]
- Electronic publications [132309]
- Faculty of Medical Sciences [93209]
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