Classification of mammograms using 2D Haar wavelets, rough sets and Support Vector Machines

R. Swiniarski, Heon Shin Joo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents an application of wavelets methods and Support Vector Machines (SVM) in mammogram recognition systems. The Mammographic images have been classified into two categories: normal and abnormal (benign and malignant). The 2D Haar wavelets have been used to extract features from the mammographic images. The feature patterns have been reduced and selected using Principal Component Analysis (PCA) and rough sets. The rough sets methods have been applied for the final selection of the pattern features. Classification of mammograms has been enhanced using Support Vector Machines.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 International Conference on Data Mining, DMIN'05
Pages65-70
Number of pages6
StatePublished - Dec 1 2005
Externally publishedYes
Event2005 International Conference on Data Mining, DMIN'05 - Las Vegas, NV, United States
Duration: Jun 20 2005Jun 23 2005

Publication series

NameProceedings of the 2005 International Conference on Data Mining, DMIN'05

Conference

Conference2005 International Conference on Data Mining, DMIN'05
CountryUnited States
CityLas Vegas, NV
Period6/20/056/23/05

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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