A computer-aided diagnosis system for breast cancer detection by using a curvelet transform | Zendy

Nebi Gedik | Zendy; Ayten Atasoy | Zendy

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A computer-aided diagnosis system for breast cancer detection by using a curvelet transform

Author(s) -

Nebi Gedik,

Ayten Atasoy

Publication year - 2013

Publication title -

turkish journal of electrical engineering and computer sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.225

H-Index - 30

eISSN - 1303-6203

pISSN - 1300-0632

DOI - 10.3906/elk-1201-8

Subject(s) - artificial intelligence , mammography , pattern recognition (psychology) , breast cancer , computer aided diagnosis , linear discriminant analysis , feature selection , curvelet , computer science , principal component analysis , feature (linguistics) , dimensionality reduction , cancer , radiology , medicine , wavelet transform , wavelet , linguistics , philosophy

The most common type of cancer among women worldwide is breast cancer. Early detection of breast cancer is very important to reduce the fatality rate. For the hundreds of mammographic images scanned by a radiologist, only a few are cancerous. While detecting abnormalities, some of them may be missed, as the detection of suspicious and abnormal images is a recurrent mission that causes fatigue and eyestrain. In this paper, a computer-aided diagnosis system using the curvelet transform (CT) algorithm is proposed for interpreting mammograms to improve the decision making. The purpose of this study is to develop a method for the characterization of the mammography as both normal and abnormal regions, and to determine its diagnostic performance to dierentiate between malignant and benign ones.

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