Open AccessDiscrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties using machine learning methods
Author(s) -
Sun Ying,
Zhang Sa,
Duan Song,
Huang Lumao,
Li Zhou,
Yu Xuefei,
Xin Sherman Xuegang
Publication year - 2020
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2019.0398
Subject(s) - dielectric , receiver operating characteristic , artificial intelligence , support vector machine , dielectric permittivity , permittivity , machine learning , pattern recognition (psychology) , computer science , materials science , mathematics , optoelectronics
Numerous researchers approved discrepancies in dielectric properties between malignant and normal tissues. Such discrepancies serve as a foundation for the development of computer‐aided diagnostic technologies. In this study, machine learning methods were proposed for discrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties. To do so, first, two independent‐sample t ‐tests and receiver operating characteristic curve analysis were utilised to examine discrimination power with respect to three types of features, namely, permittivity, conductivity and Cole–Cole fitting parameters. K ‐nearest neighbour and support vector machine classifiers were used to assess the possibility of combining these features for better classification accuracy. Obtained k ‐fold cross‐validation accuracy reached 88.2%. The obtained accuracy indicated the potential capability of discrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties.