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Clustering and least square based neural technique for learning and identification of suspicious areas within digital mammograms

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conference contribution
posted on 06.12.2017, 00:00 by NULL McLeod, Brijesh Verma
This paper presents a technique which explores the fusion of clustering and a least square method for the classification of suspicious areas within digital mammograms into benign and malignant classes. It incorporates clustering algorithm such as k-means in conjunction with a gram-schmidt based least square method. The main focus of the research presented in this paper is to (1) improve the classification of features from suspicious areas within digital mammograms and (2) examine the effects that the determined clusters and least square methods have on classification accuracy and efficiency. The proposed technique has been tested on a benchmark database and the results from preliminary experiments are discussed.

Funding

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

History

Start Page

190

End Page

194

Number of Pages

5

Start Date

01/01/2007

Finish Date

01/01/2007

ISBN-10

0769530508

Location

India

Publisher

IEEE Computer Society

Place of Publication

U.S.A.

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Business and Informatics;

Era Eligible

Yes

Name of Conference

International Conference on Computational Intelligence and Multimedia Applications

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