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

R. Swiniarski; Heon Shin Joo

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 proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings of the 2005 International Conference on Data Mining, DMIN'05
Pages	65-70
Number of pages	6
State	Published - 2005
Externally published	Yes
Event	2005 International Conference on Data Mining, DMIN'05 - Las Vegas, NV, United States Duration: Jun 20 2005 → Jun 23 2005

Publication series

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

Conference

Conference	2005 International Conference on Data Mining, DMIN'05
Country/Territory	United States
City	Las Vegas, NV
Period	6/20/05 → 6/23/05

ASJC Scopus subject areas

Computer Science Applications
Software

Cite this

Classification of mammograms using 2D Haar wavelets, rough sets and Support Vector Machines. / Swiniarski, R.; Joo, Heon Shin.
Proceedings of the 2005 International Conference on Data Mining, DMIN'05. 2005. p. 65-70 (Proceedings of the 2005 International Conference on Data Mining, DMIN'05).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Swiniarski, R & Joo, HS 2005, Classification of mammograms using 2D Haar wavelets, rough sets and Support Vector Machines. in Proceedings of the 2005 International Conference on Data Mining, DMIN'05. Proceedings of the 2005 International Conference on Data Mining, DMIN'05, pp. 65-70, 2005 International Conference on Data Mining, DMIN'05, Las Vegas, NV, United States, 6/20/05.

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AB - 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.

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

Abstract

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Conference

ASJC Scopus subject areas

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