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Breast Cancer Recognition by Support Vector Machine Combined with Daubechies Wavelet Transform and Principal Component Analysis

  • Fangyuan LiuEmail author
  • Mackenzie Brown
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

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%.

Keywords

Wavelet transform Principal component analysis Support vector machine Breast cancer 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina
  2. 2.School of EngineeringEdith Cowan UniversityJoondalupAustralia

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