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

Raquel C. Conceição; Hugo Medeiros; Martin O'Halloran; Diego Rodriguez-Herrera; Daniel Flores-Tapia; Stephen Pistorius

doi:10.1109/URSIGASS.2014.6930131

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

Raquel C. Conceição
, Hugo Medeiros
, Martin O'Halloran
, Diego Rodriguez-Herrera
, Daniel Flores-Tapia
, Stephen Pistorius

Electrical & Electronic Engineering

Universidade de Lisboa
Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa
University of Manitoba

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

18 Citations (Scopus)

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
Title of host publication	2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467352253
DOIs	https://doi.org/10.1109/URSIGASS.2014.6930131
Publication status	Published - 17 Oct 2014
Event	31st General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2014 - Beijing, China Duration: 16 Aug 2014 → 23 Aug 2014

Publication series

Name	2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014

Conference

Conference	31st General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2014
Country/Territory	China
City	Beijing
Period	16/08/14 → 23/08/14

Access to Document

10.1109/URSIGASS.2014.6930131

Cite this

Conceição, R. C., Medeiros, H., O'Halloran, M., Rodriguez-Herrera, D., Flores-Tapia, D., & Pistorius, S. (2014). SVM-based classification of breast tumour phantoms using a UWB radar prototype system. In 2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014 Article 6930131 (2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/URSIGASS.2014.6930131

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title = "SVM-based classification of breast tumour phantoms using a UWB radar prototype system",

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.",

author = "Concei{\c c}{\~a}o, \{Raquel C.\} and Hugo Medeiros and Martin O'Halloran and Diego Rodriguez-Herrera and Daniel Flores-Tapia and Stephen Pistorius",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 31st General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2014 ; Conference date: 16-08-2014 Through 23-08-2014",

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language = "English",

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Conceição, RC, Medeiros, H, O'Halloran, M, Rodriguez-Herrera, D, Flores-Tapia, D & Pistorius, S 2014, SVM-based classification of breast tumour phantoms using a UWB radar prototype system. in 2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014., 6930131, 2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014, Institute of Electrical and Electronics Engineers Inc., 31st General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2014, Beijing, China, 16/08/14. https://doi.org/10.1109/URSIGASS.2014.6930131

SVM-based classification of breast tumour phantoms using a UWB radar prototype system. / Conceição, Raquel C.; Medeiros, Hugo; O'Halloran, Martin et al.
2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 6930131 (2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014).

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

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T1 - SVM-based classification of breast tumour phantoms using a UWB radar prototype system

AU - Conceição, Raquel C.

AU - Medeiros, Hugo

AU - O'Halloran, Martin

AU - Rodriguez-Herrera, Diego

AU - Flores-Tapia, Daniel

AU - Pistorius, Stephen

PY - 2014/10/17

Y1 - 2014/10/17

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.

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U2 - 10.1109/URSIGASS.2014.6930131

DO - 10.1109/URSIGASS.2014.6930131

M3 - Conference Publication

T3 - 2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014

BT - 2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 31st General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2014

Y2 - 16 August 2014 through 23 August 2014

ER -

Conceição RC, Medeiros H, O'Halloran M, Rodriguez-Herrera D, Flores-Tapia D, Pistorius S. SVM-based classification of breast tumour phantoms using a UWB radar prototype system. In 2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014. Institute of Electrical and Electronics Engineers Inc. 2014. 6930131. (2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014). doi: 10.1109/URSIGASS.2014.6930131

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

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