Accurate Classification of Cancer in Mammogram Images

  • M. Parisa BehamEmail author
  • R. Tamilselvi
  • S. M. Mansoor Roomi
  • A. Nagaraj
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 65)


In the last decade, machine learning plays a vital role in the detection of breast cancer. Mammography is a proficient tool for early stage detection of breast cancer. In this work, a simple technique for breast cancer image classification in l mammogram images is proposed. Highly discriminant local binary patterns are extracted from the wavelet normalized mammogram images. K-nearest neighbor classifier is used to categorize the abnormal cancer cell images. A mammogram database is created to evaluate the efficacy of our algorithm. From the experimental results, the performance of our algorithms is comparatively good with very less computational time.


Mammogram database Cancer cell detection Benign and malignant LBP K-NN classifier 


Ethical Approval

The mammogram database used in this paper is provided by Pixel scans, Trichy. The ethical committee of Pixel scans has reviewed and approved to conduct research using this mammogram database and publish papers based on the results using that biomedical images.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • M. Parisa Beham
    • 1
    Email author
  • R. Tamilselvi
    • 1
  • S. M. Mansoor Roomi
    • 2
  • A. Nagaraj
    • 1
  1. 1.Department of ECESethu Institute of TechnologyVirudhunagarIndia
  2. 2.Department of ECEThiagarajar College of EngineeringMaduraiIndia

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