Breast Tissue Density Classification Using Wavelet-Based Texture Descriptors

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 381)

Abstract

It has been well established that the risk of breast cancer development is associated with increased breast density. Therefore, characterization of breast tissue density is clinically significant. In the present work, the potential of various wavelet energy descriptors (derived from ten different compact support wavelet filters) has been investigated for breast tissue density classification using kNN, SVM, and PNN classifiers. The work has been carried out on the MIAS dataset. The highest classification accuracy of 96.2 % is achieved using the kNN classifier Haar wavelet energy descriptors.

Keywords

Breast density classification Wavelet texture descriptors K-nearest neighbors Probabilistic neural network Support vector machine 

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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Biomedical Research Lab, Department of Electrical and Instrumentation EngineeringThapar UniversityPatialaIndia
  2. 2.Department of Electronics and Communication EngineeringJaypee University of Information TechnologySolanIndia

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