cqu_5530+ATTACHMENT02+ATTACHMENT02.4.pdf (193.08 kB)
Download file

A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications

Download (193.08 kB)
conference contribution
posted on 06.12.2017, 00:00 authored by Brijesh Verma, Rinku PanchalRinku Panchal, K Kumar
The paper proposes a novel min-max feature value based neural architecture and learning algorithm for classification of microcalcification patterns in digital mammograms. The neural architecture has a single hidden layer and it has a fixed number of hidden units and outputs. One class is represented by three hidden units and an output. The suspicious areas represented by chain code, are extracted from digital mammograms. The feature values are extracted for benign and malignant microcalcifications. A set of min, average and max values for every input feature is defined and assigned to the weights between input and hidden layer. The weights of the output layer are calculated using least squares methods or assigned in such a way that it maximizes the output value for only one class. Many experiments were conducted on a benchmark database of digital mammograms and comparative results are included in this paper.

History

Start Page

2033

End Page

2038

Number of Pages

6

Start Date

01/01/2003

ISSN

1098-7576

ISBN-10

0780378989

Location

Portland, Oregon

Publisher

The Institute of Electrical and Electronics Engineers

Place of Publication

United States

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Bond University (Gold Coast, Qld.); Griffith University;

Era Eligible

Yes

Name of Conference

International Joint Conference on Neural Networks;IEEE International Conference on Neural Networks