Texture Based Associative Classifier—An Application of Data Mining for Mammogram Classification

  • Deepa S. Deshpande
  • Archana M. Rajurkar
  • Ramchandra R. Manthalkar
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)


The incidence of breast cancer is rapidly becoming the number one cancer in females. It is the serious health problem and leading cause of death for middle aged women. Mammography is one of the most reliable methods for early detection of breast cancer. But mammograms are the most difficult images for interpretation and may lead to false diagnosis. Therefore there is a significant need of automatic extraction of the actionable information from the mammogram data in order to ensure improvement in diagnosis. To address this issue, we have proposed an automatic classification system for breast cancer using Texture Based Associative Classifier (TBAC). Here we wish to automatically classify the breast mammograms into three basic categories i.e. normal, benign and malignant based on their texture associations. Our experimental results on MIAS dataset demonstrate that the proposed classifier TBAC is superior to existing associative classifiers for mammogram classification.


Association rules Breast cancer Mammogram classification Associative classifier 


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Copyright information

© Springer India 2015

Authors and Affiliations

  • Deepa S. Deshpande
    • 1
  • Archana M. Rajurkar
    • 2
  • Ramchandra R. Manthalkar
    • 3
  1. 1.Department of Computer Science and EngineeringMGM’s Jawaharlal Nehru Engineering CollegeAurangabadIndia
  2. 2.Department of Computer Science and EngineeringMGM’s College of Engineering CollegeNandedIndia
  3. 3.Department of Electronics and Telecommunication EngineeringSGGS Institute of Engineering and TechnologyNandedIndia

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