Breast-Cancer identification using HMM-fuzzy approach

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Hassan, R, Hossain, M, Begg, Rezaul, Ramamohanarao, Kotagiri and Morsi, Y (2010) Breast-Cancer identification using HMM-fuzzy approach. Computers in Biology and Medicine, 40 (3). pp. 240-251. ISSN 0010-4825

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.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/7226
DOI https://doi.org/10.1016/j.compbiomed.2009.11.003
Official URL http://www.sciencedirect.com/science/article/pii/S...
Subjects Historical > Faculty/School/Research Centre/Department > Institute of Sport, Exercise and Active Living (ISEAL)
Historical > FOR Classification > 0903 Biomedical Engineering
Historical > SEO Classification > 970111 Expanding Knowledge in the Medical and Health Sciences
Keywords ResPubID20262, classification, feature selection, receiver operating characteristics (ROC), hidden Markov model (HMM), fuzzy logic
Citations in Scopus 28 - View on Scopus
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