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Neural classification of mass abnormalities with different types of features in digital mammography

journal contribution
posted on 06.12.2017, 00:00 by Rinku Panchal, Brijesh Verma
Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BIRADSfeatures, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

Funding

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

History

Volume

6

Issue

1

Start Page

61

End Page

75

Number of Pages

15

ISSN

1469-0268

Location

Singapore

Publisher

World Scientific Publishing

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Business and Informatics; TBA Research Institute;

Era Eligible

Yes

Journal

International journal of computational intelligence and applications.

Exports