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Combining SOM based clustering and MGS for classification of suspicious areas within digital mammograms

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conference contribution
posted on 06.12.2017, 00:00 by NULL McLeodNULL McLeod, Brijesh VermaBrijesh Verma, Rinku PanchalRinku Panchal
The fusion of clustering and least square based method for the classification of suspicious areas into benign and malignant classes in digital mammograms was investigated in our previous paper which showed some promising results. This paper extends the investigation by combining a self organising map (SOM) based clustering with modified gram-schmidt (MGS) method. The main focus of the research presented in this paper is to investigate the effect that the assignment of input weights from the SOM clustering algorithm have on the efficiency and accuracy of the neural network classifier. A number of experiments have been conducted on a benchmark database. A comparative analysis with our previous results and other known techniques in the literature is presented in this paper.

Funding

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

History

Start Page

413

End Page

418

Number of Pages

6

Start Date

01/01/2007

ISBN-10

1424415020

Location

Melbourne, 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