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Multi-cluster support vector machine classifier for the classification of suspicious areas in digital mammograms

journal contribution
posted on 06.12.2017, 00:00 by NULL McLeodNULL McLeod, Brijesh VermaBrijesh Verma
This paper presents a novel technique for the classification of suspicious areas in digital mammograms. The proposed technique is based on a novel idea of clustering input data into numerous (soft) clusters and amalgamating them with a Support Vector Machine (SVM) classifier. The technique is called Multi-Cluster Support Vector Machine (MCSVM) and is designed to provide a fast converging technique with good generalization abilities leading to an improved classification as a benign or malignant class. The proposed SCSVM technique has been evaluated on data from the DDSM benchmark database. The experimental results showed that the proposed MCSVM classifier achieves better results than standard SVM. A paired t-test and Anova analysis showed that the results are statistically significant.

History

Volume

10

Issue

4

Start Page

481

End Page

494

Number of Pages

14

eISSN

1757-5885

ISSN

1469-0268

Location

Singapore

Publisher

Imperial College Press/World Scientific Publishing Company

Language

en-aus

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Journal

International journal of computational intelligence and applications.