Authors:
Tingting Mu
and
Asoke K. Nandi
Affiliation:
The University of Liverpool, United Kingdom
Keyword(s):
Breast cancer, diagnosis, prognosis, pattern classification, kernel method.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
The medical applications of several advanced, kernel-based classifiers to breast cancer diagnosis and prognosis are studied and compared in this paper, including kernel Fisher’s discriminative analysis, support vector machines (SVMs), multisurface proximal SVMs, as well as the pairwise Rayleigh quotient classifier and the strict 2-surface proximal classifier that we recently proposed. The radial basis function kernel is employed to incorporate nonlinearity. Studies are conducted with the Wisconsin diagnosis and prognosis breast cancer datasets generated from fine-needle-aspiration samples by image processing. Comparative analysis is provided in terms of classification accuracy, computing time, and sensitivity to the regularization parameters for the above classifiers.