Experimental Breast Tumor Detection Using Nn-Based Uwb Imaging

This paper presents a system with experimental comple-ment to a simulation work for early breast tumor detection. The ex-periments are conducted using commercial Ultrawide-Band (UWB) transceivers, Neural Network (NN) based Pattern Recognition (PR) software for imaging and proposed breast phantoms f...

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Main Author: Sabira, Khatun
Format: Article
Language:English
Published: 2011
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/2063/1/Experimental_Breast_Tumor_Detection_Using_NN-Based_UWB_Imaging_-_Dr.Sabira_Khatun-Journal-.PDF
http://umpir.ump.edu.my/id/eprint/2063/
http://www.jpier.org/PIER/pier.php?paper=10110102
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spelling my.ump.umpir.20632018-10-11T04:24:17Z http://umpir.ump.edu.my/id/eprint/2063/ Experimental Breast Tumor Detection Using Nn-Based Uwb Imaging Sabira, Khatun QA75 Electronic computers. Computer science This paper presents a system with experimental comple-ment to a simulation work for early breast tumor detection. The ex-periments are conducted using commercial Ultrawide-Band (UWB) transceivers, Neural Network (NN) based Pattern Recognition (PR) software for imaging and proposed breast phantoms for homogenous and heterogeneous tissues. The proposed breast phantoms (homoge-neous and heterogeneous) and tumor are constructed using available low cost materials and their mixtures with minimal e®ort. A speci¯c glass is used as skin. All the materials and their mixtures are con-sidered according to the ratio of the dielectric properties of the breast tissues. Experiments to detect tumor are performed in regular noisy room environment. The UWB signals are transmitted from one side of the breast phantom (for both cases) and received from opposite side diagonally repeatedly. Using discrete cosine transform (DCT) of these received signals, a Neural Network (NN) module is developed, trained and tested. The tumor existence, size and location detection rates for both cases are highly satisfactory, which are approximately: (i) 100%,95.8% and 94.3% for homogeneous and (ii) 100%, 93.4% and 93.1% for heterogeneous cases respectively. This gives assurance of early de- tection and the practical usefulness of the developed system in near future. 2011 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/2063/1/Experimental_Breast_Tumor_Detection_Using_NN-Based_UWB_Imaging_-_Dr.Sabira_Khatun-Journal-.PDF Sabira, Khatun (2011) Experimental Breast Tumor Detection Using Nn-Based Uwb Imaging. Progress In Electromagnetics Research (PIER), 111. pp. 447-465. ISSN ISSN: 1070-4698, E-ISSN: 1559-8985 http://www.jpier.org/PIER/pier.php?paper=10110102
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Sabira, Khatun
Experimental Breast Tumor Detection Using Nn-Based Uwb Imaging
description This paper presents a system with experimental comple-ment to a simulation work for early breast tumor detection. The ex-periments are conducted using commercial Ultrawide-Band (UWB) transceivers, Neural Network (NN) based Pattern Recognition (PR) software for imaging and proposed breast phantoms for homogenous and heterogeneous tissues. The proposed breast phantoms (homoge-neous and heterogeneous) and tumor are constructed using available low cost materials and their mixtures with minimal e®ort. A speci¯c glass is used as skin. All the materials and their mixtures are con-sidered according to the ratio of the dielectric properties of the breast tissues. Experiments to detect tumor are performed in regular noisy room environment. The UWB signals are transmitted from one side of the breast phantom (for both cases) and received from opposite side diagonally repeatedly. Using discrete cosine transform (DCT) of these received signals, a Neural Network (NN) module is developed, trained and tested. The tumor existence, size and location detection rates for both cases are highly satisfactory, which are approximately: (i) 100%,95.8% and 94.3% for homogeneous and (ii) 100%, 93.4% and 93.1% for heterogeneous cases respectively. This gives assurance of early de- tection and the practical usefulness of the developed system in near future.
format Article
author Sabira, Khatun
author_facet Sabira, Khatun
author_sort Sabira, Khatun
title Experimental Breast Tumor Detection Using Nn-Based Uwb Imaging
title_short Experimental Breast Tumor Detection Using Nn-Based Uwb Imaging
title_full Experimental Breast Tumor Detection Using Nn-Based Uwb Imaging
title_fullStr Experimental Breast Tumor Detection Using Nn-Based Uwb Imaging
title_full_unstemmed Experimental Breast Tumor Detection Using Nn-Based Uwb Imaging
title_sort experimental breast tumor detection using nn-based uwb imaging
publishDate 2011
url http://umpir.ump.edu.my/id/eprint/2063/1/Experimental_Breast_Tumor_Detection_Using_NN-Based_UWB_Imaging_-_Dr.Sabira_Khatun-Journal-.PDF
http://umpir.ump.edu.my/id/eprint/2063/
http://www.jpier.org/PIER/pier.php?paper=10110102
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score 13.18916