DocumentCode
1563383
Title
Discrimination Methods for the Classification of Breast Cancer Diagnosis
Author
Shou-kui, Si ; Xiao-feng, Wang ; Xi-jing, Sun
Author_Institution
Dept. of Basic Sci., Naval Aeronaut. Eng. Acad., Yantai
Volume
1
fYear
2005
Firstpage
259
Lastpage
261
Abstract
A reliable and precise classification of breast cancer is essential for successful diagnosis. Discrimination methods, including mahalanobis distance, Fisher rules and support vector machine, are applied for the classification of breast cancer diagnosis. This article compares the performance of different discrimination methods
Keywords
biological tissues; cancer; cellular biophysics; fault diagnosis; pattern classification; support vector machines; Fisher rules; breast cancer diagnosis classification; discrimination methods; mahalanobis distance; support vector machine; Aerospace engineering; Breast cancer; Breast neoplasms; Computer simulation; Covariance matrix; Machine learning; Medical diagnostic imaging; Sun; Support vector machine classification; Support vector machines; Fisher discrimination; Mahalanobis distances; classification; discrimination method; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location
Beijing
Print_ISBN
0-7803-9422-4
Type
conf
DOI
10.1109/ICNNB.2005.1614610
Filename
1614610
Link To Document