Title

Breast cancer recognition by support vector machine combined with daubechies wavelet transform and principal component analysis

Document Type

Book Chapter

ISSN

22129391

Publisher

Springer Netherlands

School

School of Engineering

Comments

Originally published as: Liu, F., & Brown, M. (2019). Breast cancer recognition by support vector machine combined with daubechies wavelet transform and principal component analysis. Original chapter available here

Abstract

The method of identifying the abnormal mammary gland tumor images was presented in order to assist the medical staff to find the patients with breast diseases accurately and timely. Db2 wavelet transform and principal component analysis (select the optimal threshold) is used to extract the effective features, support vector machine (set appropriate penalty parameter) is used to classify health and diseased samples, and 10-fold cross-validation is used to verify the classification result. The experimental results show that the method is feasible, the average sensitivity is 83.10 ± 1.91%, the average specificity is 82.60 ± 4.50%, and the average accuracy is 82.85 ± 2.21%.

DOI

10.1007/978-3-030-00665-5_177

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