DocumentCode :
743585
Title :
Pilot study: electrical impedance based tissue classification using support vector machine classifier
Author :
Grewal, Parvind Kaur ; Golnaraghi, Farid
Author_Institution :
Sch. of Eng. Sci., Simon Fraser Univ., Surrey, BC, Canada
Volume :
8
Issue :
6
fYear :
2014
Firstpage :
579
Lastpage :
587
Abstract :
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.
Keywords :
biological tissues; data handling; data mining; decision making; electric impedance imaging; learning (artificial intelligence); mammography; medical diagnostic computing; pattern classification; support vector machines; SVM sensitivity improvement; automated decision making; clinical diagnosis; computer aided diagnosis; data binning; data mining-based decision making system; data pruning; electrical impedance measuring system reliability; electrical impedance spectroscopy system; electrical impedance-based tissue classification; impedance loci spectral features; machine learning methodologies; malignant breast tissue classification; structural risk minimisation; supervised learning models; support vector machine classifier;
fLanguage :
English
Journal_Title :
Science, Measurement & Technology, IET
Publisher :
iet
ISSN :
1751-8822
Type :
jour
DOI :
10.1049/iet-smt.2013.0087
Filename :
6985792
Link To Document :
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