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Impact of multiple clusters on neural classification of ROIs in digital mammograms

conference contribution
posted on 22.03.2022, 00:08 by Brijesh VermaBrijesh Verma
This paper evaluates the impact of multiple clusters on neural classification of regions of interest (ROIs) in digital mammograms. The training and test sets for neural networks usually contain inputs extracted from ROIs and relevant class such as benign and malignant. However, the patterns such as regions of interest in digital mammograms do not have just one cluster per class instead they have many clusters within benign and malignant classes. Therefore, neural network training may benefit in terms of accuracy and efficiency by creating and analyzing a number of clusters within a class. A novel multiple clusters based neural classification approach is presented. In this approach, input data is clustered into a number of clusters per class and a neural classifier is trained with clustered data which contain multiple clusters per class. The experiments on a benchmark database of digital mammograms are conducted. The results show that the multiple clusters per class have significant impact on neural classification and overall they achieve better accuracy than single cluster per class based classification of ROIs in digital mammograms.

History

Start Page

3220

End Page

3230

Number of Pages

11

Start Date

14/06/2009

Finish Date

19/06/2009

eISSN

1098-7576

ISBN-13

9781424435531

Location

Atlanta, Georgia, USA

Publisher

IEEE

Place of Publication

NJ, 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

2009 International Joint Conference on Neural Networks

Parent Title

Proceedings of International Joint Conference on Neural Networks