2014 XXXITH URSI GENERAL ASSEMBLY AND SCIENTIFIC SYMPOSIUM (URSI GASS)

Martin O'Halloran

2014 XXXITH URSI GENERAL ASSEMBLY AND SCIENTIFIC SYMPOSIUM (URSI GASS)

Martin O'Halloran

Electrical & Electronic Engineering

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

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.

Original language	English (Ireland)
Title of host publication	SVM-based Classification of Breast Tumour Phantoms Using a UWB Radar Prototype System
Publication status	Published - 1 Jan 2014

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Conceicao, RC,Medeiros, H,O'Halloran, M,Rodriguez-Herrera, D,Flores-Tapia, D,Pistorius, S,

Cite this

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title = "2014 XXXITH URSI GENERAL ASSEMBLY AND SCIENTIFIC SYMPOSIUM (URSI GASS)",

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.",

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N2 - 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.

AB - 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.

M3 - Conference Publication

BT - SVM-based Classification of Breast Tumour Phantoms Using a UWB Radar Prototype System

ER -

2014 XXXITH URSI GENERAL ASSEMBLY AND SCIENTIFIC SYMPOSIUM (URSI GASS)

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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