Effective Classification of Microcalcification Clusters Using Improved Support Vector Machine with Optimised Decision Making

Author

Jinchang Ren ; Zheng Wang ; Meijun Sun ; Soraghan, John

Author_Institution

Centre for excellence in Signal & Image Process., Univ. of Strathclyde, Glasgow, UK

fYear

2013

fDate

26-28 July 2013

Firstpage

390

Lastpage

393

Abstract

Classification of micro calcification clusters is very essential for early detection of breast cancer from mammograms. In this paper, an improved support vector machine (SVM) scheme is proposed, where optimized decision making is introduced for effective and more accurate data classification. Experimental results on the well-known DDSM database have shown that the proposed method can significantly increase the performance in terms of F1 and Az measurements for the successful classification of clustered micro calcifications.

Keywords

cancer; decision making; image classification; mammography; medical image processing; object detection; support vector machines; visual databases; Az measurements; DDSM database; F1 measurements; SVM scheme; breast cancer early detection; data classification; decision making optimization; mammogram; microcalcification cluster classification; support vector machine; Breast cancer; Decision making; Feature extraction; Kernel; Support vector machines; Training; computer-aided diagnosis; mammography; microcalification clusters (MCC); optimized decision making; support vector machine (SVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Graphics (ICIG), 2013 Seventh International Conference on

Conference_Location

Qingdao

Type

conf

DOI

10.1109/ICIG.2013.84

Filename

6643702