Comparative evaluation of support vector machine classification for computer aided detection of breast masses in mammography
Publication year
2012Source
Physics in Medicine and Biology, 57, (2012), pp. 5295-5307ISSN
Publication type
Article / Letter to editor

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Organization
Radiology
Data Science
Journal title
Physics in Medicine and Biology
Volume
vol. 57
Page start
p. 5295
Page end
p. 5307
Subject
Data Science; ONCOL 5: Aetiology, screening and detectionAbstract
False positive (FP) marks represent an obstacle for effective use of computer-aided detection (CADe) of breast masses in mammography. Typically, the problem can be approached either by developing more discriminative features or by employing different classifier designs. In this paper, the usage of support vector machine (SVM) classification for FP reduction in CADe is investigated, presenting a systematic quantitative evaluation against neural networks, k-nearest neighbor classification, linear discriminant analysis and random forests. A large database of 2516 film mammography examinations and 73 input features was used to train the classifiers and evaluate for their performance on correctly diagnosed exams as well as false negatives. Further, classifier robustness was investigated using varying training data and feature sets as input. The evaluation was based on the mean exam sensitivity in 0.05-1 FPs on normals on the free-response receiver operating characteristic curve (FROC), incorporated into a tenfold cross validation framework. It was found that SVM classification using a Gaussian kernel offered significantly increased detection performance (P = 0.0002) compared to the reference methods. Varying training data and input features, SVMs showed improved exploitation of large feature sets. It is concluded that with the SVM-based CADe a significant reduction of FPs is possible outperforming other state-of-the-art approaches for breast mass CADe.
This item appears in the following Collection(s)
- Academic publications [202786]
- Faculty of Medical Sciences [80017]
- Faculty of Science [31851]
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