Using neural networks to select wavelet features for breast cancer diagnosis

This study focuses on improving microcalcification classification by establishing an efficient computer-aided diagnosis system that extracts Daubechies-4 and biorthogonal wavelet features. These wavelets were chosen because they have been used in military target recognition and fingerprint recogniti...

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Bibliographic Details
Published in:	IEEE engineering in medicine and biology magazine Vol. 15; no. 3; pp. 95 - 102
Main Authors:	Kocur, C.M., Rogers, S.K., Myers, L.R., Burns, T., Kabrisky, M., Hoffmeister, J.W., Bauer, K.W., Steppe, J.M.
Format:	Journal Article
Language:	English
Published:	IEEE 01-05-1996
Subjects:	Artificial neural networks Biomedical signal processing Breast cancer Computer aided diagnosis Fingerprint recognition Image texture Mammography Military computing Neural networks Target recognition
Online Access:	Get full text
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Summary:	This study focuses on improving microcalcification classification by establishing an efficient computer-aided diagnosis system that extracts Daubechies-4 and biorthogonal wavelet features. These wavelets were chosen because they have been used in military target recognition and fingerprint recognition research with images characterized by low contrast, similar to mammography. Feature selection techniques are employed to further increase classification performance. The artificial neural network feature selection techniques are complemented by a conventional decision boundary-based feature selection method. The results using the wavelet features are compared to more conventional measures of image texture, angular second moment, and Karhunen Loeve coefficients. The use of alternative signal processing to compare wavelet and neural techniques allows for a measure of the problem difficulty. It is concluded that advances and contributions have been made with the introduction of two novel feature extraction methods for breast cancer diagnosis, wavelets and eigenmasses. Additionally, feature selection techniques are demonstrated, compared, and validated, transforming adequate discrimination power into promising classification results.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0739-5175
DOI:	10.1109/51.499766