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A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM
Author
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-09
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
In digital mammograms, an early sign of breast cancer is the existence of microcalcification clusters (MCs), which is very important to the early breast cancer detection.
In this paper, a new approach is proposed to classify and detect MCs.
We formulate this classification problem as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a “vocabulary” of visual parts.
A visual information-rich vocabulary of training samples is manually built up from a set of samples, which include MCs parts and no-MCs parts.
With the prior ground truth of MCs in mammograms, the sparse feature learning is acquired by the l P -regularized least square approach with the interior-point method.
Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs).
To investigate its performance, the proposed method is applied to DDSM datasets and compared with support vector machines (SVMs) with the same dataset.
Experiments have shown that performance of the proposed method is more efficient or better than the state-of-art methods.
American Psychological Association (APA)
Zhang, Xin-Sheng. 2014. A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1051808
Modern Language Association (MLA)
Zhang, Xin-Sheng. A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM. The Scientific World Journal No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1051808
American Medical Association (AMA)
Zhang, Xin-Sheng. A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1051808
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051808