6 April 1995 Wavelets for computer-aided breast cancer diagnosis
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Abstract
More than 50 million women over the age of 40 are currently at risk for breast cancer in the United States. Computer-aided diagnosis, used as a `second opinion' to radiologists, will aid in decreasing the number of false readings of mammograms. A novel feature extraction method is presented that provides increased classification power. Wavelets, previously only exploited for their segmentation benefits, are explored as features for classification. Daubechies4, Daubechies20, and biorthogonal wavelets are each investigated. Applied to 94 difficult-to- diagnose digitized microcalcification cases, performance is 74 percent correct classifications. Feature selection techniques are presented which further improve wavelet classification performance to 88 percent correct classification.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lemuel R. Myers, Catherine M. Kocur, Steven K. Rogers, Chris Eisenbies, Jeffrey W. Hoffmeister, "Wavelets for computer-aided breast cancer diagnosis", Proc. SPIE 2491, Wavelet Applications II, (6 April 1995); doi: 10.1117/12.205438; https://doi.org/10.1117/12.205438
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