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A classifier with clustered sub classes for the classification of suspicious areas in digital mammograms

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conference contribution
posted on 06.12.2017, 00:00 authored by NULL McLeodNULL McLeod, Brijesh Verma
This paper presents a novel methodology for the classification of suspicious areas in digital mammograms. The methodology is based on the fusion of clustered sub classes with various intelligent classifiers. A number of classifiers have been incorporated into the proposed methodology and evaluated on the well known benchmark digital database of screening mammography (DDSM). The results in the form of overall classification accuracies, TP, TN, FP and FN have been analyzed, compared and presented. The results of all four tested classifiers with clustered sub classes on the DDSM benchmark database show that the proposed methodology can significantly improve the accuracy and reduce the false positive rate.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Page

31

End Page

38

Number of Pages

8

Start Date

18/07/2010

Finish Date

23/07/2010

ISBN-13

9781424469178

Location

Barcelona, Spain

Publisher

IEEE

Place of Publication

USA

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Arts, Business, Informatics and Education; Institute for Resource Industries and Sustainability (IRIS);

Era Eligible

Yes

Name of Conference

International Joint Conference on Neural Networks (IJCNN 2010)

Parent Title

2010 International Joint Conference on Neural Networks (IJCNN) , associated with the 2010 IEEE World Congress on Computational Intelligence (IEEE WCCI 2010)2010 IEEE World Congress on Computational Intelligence (IEEE WCCI 2010)., 18-23 July 2010, Barcelona, Spain.