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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


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.

Original languageEnglish
Title of host publicationIntelligent Systems Design and Applications - 17th International Conference on Intelligent Systems Design and Applications ISDA 2017
EditorsAjith Abraham, Niketa Gandhi, Pranab Kr. Muhuri, Azah Kamilah Muda
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319763477
StatePublished - 2018

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357

Bibliographical note

Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.


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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science


Dive into the research topics of 'Breast Density Classification for Cancer Detection Using DCT-PCA Feature Extraction and Classifier Ensemble'. Together they form a unique fingerprint.

Cite this