Breast-Cancer identification using HMM-fuzzy approach

Authors:
Md. Rafiul Hassan;M. Maruf Hossain;Rezaul Karim Begg;Kotagiri Ramamohanarao;Yos Morsi
Affiliations:
Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia;Center for Ageing, Rehabilitation, Exercise and Sport, Victoria University, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia;Department of Biomechanical Engineering, Swinburne University of Technology, Australia
Venue:
Computers in Biology and Medicine
Year:
2010

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Abstract

This paper presents an ensemble of feature selection and classification technique for classifying two types of breast lesion, benign and malignant. Features are selected based on their area under the ROC curves (AUC) which are then classified using a hybrid hidden Markov model (HMM)-fuzzy approach. HMM generated log-likelihood values are used to generate minimized fuzzy rules which are further optimized using gradient descent algorithms in order to enhance classification performance. The developed model is applied to Wisconsin breast cancer dataset to test its performance. The results indicate that a combination of selected features and the HMM-fuzzy approach can classify effectively the lesion types using only two fuzzy rules. Our experimental results also indicate that the proposed model can produce better classification accuracy when compared to most other computational tools.