A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM
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...
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| Format: | Online |
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Hindawi Publishing Corporation
2014
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3934082/ |
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pubmed-39340822014-04-24 A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM Zhang, Xin-Sheng Research Article 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. Hindawi Publishing Corporation 2014-02-09 /pmc/articles/PMC3934082/ /pubmed/24764773 http://dx.doi.org/10.1155/2014/970287 Text en Copyright © 2014 Xin-Sheng Zhang. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Zhang, Xin-Sheng |
| spellingShingle |
Zhang, Xin-Sheng A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM |
| author_facet |
Zhang, Xin-Sheng |
| author_sort |
Zhang, Xin-Sheng |
| title |
A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM |
| title_short |
A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM |
| title_full |
A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM |
| title_fullStr |
A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM |
| title_full_unstemmed |
A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM |
| title_sort |
new approach for clustered mcs classification with sparse features learning and twsvm |
| description |
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. |
| publisher |
Hindawi Publishing Corporation |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3934082/ |
| _version_ |
1612061684015300608 |