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
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;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614610
