Breast Density Classification for Cancer Detection Using DCT-PCA Feature Extraction and Classifier Ensemble

  • Md Sarwar Morshedul Haque
  • Md Rafiul Hassan
  • G. M. BinMakhashen
  • A. H. Owaidh
  • Joarder Kamruzzaman
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN.

Keywords

Breast cancer Breast Dense and Fatty DCT PCA Machine learning tools Pattern Recognition 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Md Sarwar Morshedul Haque
    • 1
  • Md Rafiul Hassan
    • 1
  • G. M. BinMakhashen
    • 1
  • A. H. Owaidh
    • 1
  • Joarder Kamruzzaman
    • 2
    • 3
  1. 1.King Fahd University of Petroleum and MineralsDhahranKingdom of Saudi Arabia
  2. 2.Federation University AustraliaBallaratAustralia
  3. 3.Monash UniversityMelbourneAustralia

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