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A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography

conference contribution
posted on 06.12.2017, 00:00 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. However, it is very difficult to distinguish benign and malignant cases, especially for the small size lesions in the early stage of cancer. 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. This paper proposes a neural-genetic algorithm for feature selection in conjunction with neural network based classifier. It also combined the computer-extracted statistical features from the mammogram with the human-extracted features for classifying different types of small size breast abnormalities, It obtained 90.5% accuracy rate for calcification cases and 81.2% for mass cases with different feature subsets. The obtained results show that different types of breast abnormality should use different features for classification.

Funding

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

History

Start Page

2303

End Page

2308

Number of Pages

6

Start Date

01/01/2004

ISBN-10

0780383605

Location

Budapest, Hungary

Publisher

Institute of Electrical and Electronics Engineers, Inc.

Place of Publication

Piscataway, NJ, USA

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Bond University (Gold Coast, Qld.); Faculty of Informatics and Communication; TBA Research Institute;

Era Eligible

Yes

Name of Conference

IEEE International Conference on Neural Networks;IEEE International Conference on Fuzzy Systems

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