Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/25427

Title

HMM-fuzzy model for breast cancer diagnosis

Author(s)

Hassan, M. Rafiul;  Begg, R. K.;  Morsi, Yos S.;  Lynch, Kate

Abstract

This paper presents a Hidden Markov Model (HMM) based fuzzy model for breast cancer recognition and classification. The feasibility of using HMM for generating a minimum number of fuzzy rules for a given problem has been introduced in a recent study. In this study, we apply this new approach to investigate whether or not it can differentiate effectively the two important breast cancer types: benign and malignant. Artificial neural networks (ANNs) have been proposed to improve the diagnosis of breast cancers. Fuzzy logics have the advantage compared to the ANNs in that it (Fuzzy model) is interpretable and can represent the solution of a problem with reduced computational complexity. In this paper, we demonstrate the suitability on the use of fuzzy model over ANNs for breast cancer diagnosis, as the model was able to provide a comparable classification performance (accuracy) with reduced complexity.

Publication type

Conference paper

Research centre

Swinburne University of Technology. Faculty of Engineering and Industrial Sciences. Industrial Research Institute Swinburne

Source

Proceedings XVth International Conference on Mechanics in Medicine and Biology (ICMMB-15 2006), Singapore, 06-08 December 2006, p. 272-275

Publication year

2006

Publisher

Nanyang Technological University

ISBN

1930746059