Discrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties using machine learning methods

被引:0
作者
Sun, Ying [1 ,2 ]
Zhang, Sa [1 ]
Duan, Song [3 ]
Huang, Lumao [1 ]
Li, Zhou [4 ]
Yu, Xuefei [1 ]
Xin, Sherman Xuegang [5 ]
机构
[1] Southern Med Univ, Sch Biomed Engn, Guangzhou, Peoples R China
[2] Shanghai Jiao Tong Univ, Shanghai Mental Hlth Ctr, Shanghai Key Lab Psychot Disorders, Shanghai, Peoples R China
[3] Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Dept Radiat Oncol, Guangzhou, Peoples R China
[4] Southern Med Univ, Zhujiang Hosp, Dept Gen Surg, Guangzhou, Peoples R China
[5] South China Univ Technol, Sch Med, Guangzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
image classification; feature extraction; biological tissues; support vector machines; medical image processing; learning (artificial intelligence); pattern classification; cancer; permittivity; normal tissues; malignant colorectal tissues; dielectric properties; machine learning methods; malignant tissues; computer-aided diagnostic technologies; neighbour; support vector machine classifiers; ELECTRICAL-PROPERTIES; BIOLOGICAL TISSUES; CARCINOMA; CLASSIFICATION; PARAMETERS; DIAGNOSIS;
D O I
10.1049/iet-smt.2019.0398
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:770 / 775
页数:6
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