SVM-based classification of breast tumour phantoms using a UWB radar prototype system

Author

Conceicao, Raquel C. ; Medeiros, Hudson ; O´Halloran, M. ; Rodriguez-Herrera, Diego ; Flores-Tapia, Daniel ; Pistorius, Stephen

Author_Institution

Inst. de Eng. Biofisica e Biomed., Univ. de Lisboa, Lisbon, Portugal

fYear

2014

fDate

16-23 Aug. 2014

Firstpage

1

Lastpage

4

Abstract

In this paper, a follow-up study exploring the classification of phantoms mimicking benign and malignant breast tumours, using a pre-clinical Ultra Wideband (UWB) prototype imaging system, is presented. A database of 13 benign and 13 malignant tumour phantoms was created using material which mimicked the dielectric properties of tumour tissues in the 1-6GHz frequency range. The classification was performed using a machine learning algorithm - Support Vector Machines (SVM) - and the results were compared to those of a previous study by the authors where Linear Discriminant Analysis and Quadratic Discriminant Analysis were considered.

Keywords

bioelectric phenomena; biological organs; image classification; medical image processing; microwave imaging; phantoms; support vector machines; tumours; ultra wideband radar; SVM-based classification; UWB prototype imaging system; UWB radar prototype system; benign breast tumours; benign tumour phantoms; breast tumour phantom; dielectric properties; machine learning algorithm; malignant breast tumours; malignant tumour phantoms; preclinical ultrawideband prototype imaging system; support vector machines; tumour tissues; Breast cancer; Microwave theory and techniques; Phantoms; Support vector machines; Tumors;

fLanguage

English

Publisher

ieee

Conference_Titel

General Assembly and Scientific Symposium (URSI GASS), 2014 XXXIth URSI

Conference_Location

Beijing

Type

conf

DOI

10.1109/URSIGASS.2014.6930131

Filename

6930131