期刊
IET SCIENCE MEASUREMENT & TECHNOLOGY
卷 8, 期 6, 页码 579-587出版社
WILEY
DOI: 10.1049/iet-smt.2013.0087
关键词
pattern classification; support vector machines; decision making; learning (artificial intelligence); data handling; data mining; electric impedance imaging; biological tissues; mammography; medical diagnostic computing; electrical impedance-based tissue classification; support vector machine classifier; computer aided diagnosis; automated decision making; clinical diagnosis; malignant breast tissue classification; impedance loci spectral features; machine learning methodologies; supervised learning models; structural risk minimisation; data binning; data pruning; SVM sensitivity improvement; electrical impedance measuring system reliability; data mining-based decision making system; electrical impedance spectroscopy system
资金
- Natural Sciences and Engineering Council of Canada (NSERC)
Tissue classification using computer aided diagnosis can help automated decision making to aid clinical diagnosis. Classification of breast tissue based on spectral features of impedance loci has frequently been done to classify malignant tissue with further requirement of more complex classification methodologies needed to improve the characterisation. In current study, tissue classification is done using in vivo electrical impedance data of 18 human subjects, from four quadrants of breast, palm, nail, arm, bicep and classified using algorithms involving machine learning methodologies, specifically support vector machines (SVMs) that are supervised learning models. They consist of learning algorithms based on the principal of structural risk minimisation. Two methodologies of SVM have been used in this study: with data binning and data pruning and without data binning and data pruning. Data binning and data pruning have improved the sensitivity of the SVM from 76.76 to 89.23%, but the specificity has decreased from 76.23 to 74.15%. This is a pilot study towards testing the reliability of the developed electrical impedance measuring system and developing a data mining-based decision making system into an electrical impedance spectroscopy system, to help users (physicians) with tissue classification leading to reliable objective decision making.
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