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Impact of soft clustering on 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 VermaBrijesh Verma
This paper investigates a soft cluster based approach for determining the impact of soft clustering on the training of a neural network classifier for the classification of suspicious areas in digital mammograms. An approach is proposed that first creates soft clusters for each available class and then uses soft clusters to form subclasses within benign and malignant classes. The incorporation of soft clusters in the classification process is designed to increase the learning abilities and improve the accuracy of the classification system. The experiments using soft clusters based proposed approach and a standard neural network classifier have been conducted on a benchmark database. The results have been analysed and presented in this paper.

Funding

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

History

Start Page

109

End Page

114

Number of Pages

6

Start Date

01/01/2008

Finish Date

01/01/2008

ISBN-13

9781424429578

Location

Sydney, Australia

Publisher

IEEE

Place of Publication

USA

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Business and Informatics;

Era Eligible

Yes

Name of Conference

Intelligent Sensors, Sensor Networks & Information Processing Conference.