File(s) not publicly available

Neural vs statistical classifier in conjunction with genetic algorithm based feature selection

journal contribution
posted on 06.12.2017, 00:00 authored by P Zhang, Brijesh VermaBrijesh Verma, K Kumar
Digital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas containing benign and malignant microcalcifications. However, it is very difficult to distinguish benign and malignant microcalcifications. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists in assessment of microcalcifications. The research in this paper proposes and investigates a neural-genetic algorithm for feature selection in conjunction with neural and statistical classifiers to classifymicrocalcification patterns in digitalmammograms. The obtained results show that the proposed approach is able to find an appropriate feature subset and neural classifier achieves better results than two statistical modes.

Funding

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

History

Volume

26

Issue

7

Start Page

909

End Page

919

Number of Pages

11

eISSN

0167-8655

Location

Netherlands

Publisher

Elsevier

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Informatics and Communication; School of Information Technology; TBA Research Institute;

Era Eligible

Yes

Journal

Pattern recognition letters.