3D experimental detection and discrimination of malignant and benign breast tumor using NN-based UWB imaging system

This paper presents both simulation and experimental study to detect and locate breast tumors along with their classification as malignant and/or benign in three dimensional (3D) breast model. The contrast between the dielectric properties of these two tumor types is the main key. These dielectric p...

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Published in:Progress in Electromagnetics Research
Main Author: Alshehri S.A.; Khatun S.; Jantan A.B.; Abdullah R.S.A.R.; Mahmud R.; Awang Z.
Format: Article
Language:English
Published: 2011
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-79958087925&doi=10.2528%2fPIER11022601&partnerID=40&md5=fda799d8e117509e8cb9e88b2a084c8d
id 2-s2.0-79958087925
spelling 2-s2.0-79958087925
Alshehri S.A.; Khatun S.; Jantan A.B.; Abdullah R.S.A.R.; Mahmud R.; Awang Z.
3D experimental detection and discrimination of malignant and benign breast tumor using NN-based UWB imaging system
2011
Progress in Electromagnetics Research
116

10.2528/PIER11022601
https://www.scopus.com/inward/record.uri?eid=2-s2.0-79958087925&doi=10.2528%2fPIER11022601&partnerID=40&md5=fda799d8e117509e8cb9e88b2a084c8d
This paper presents both simulation and experimental study to detect and locate breast tumors along with their classification as malignant and/or benign in three dimensional (3D) breast model. The contrast between the dielectric properties of these two tumor types is the main key. These dielectric properties are mainly controlled by the water and blood content of tumors. For simulation, electromagnetic simulator software is used. The experiment is conducted using commercial Ultrawide-Band (UWB) transceivers, Neural Network (NN) based Pattern Recognition (PR) software for imaging and homogenous breast phantom. The 3D homogeneous breast phantom and tumors are fabricated using pure petroleum jelly and a mixture of wheat flour and water respectively. The simulation and experimental setups are performed by transmitting the UWB signals from one side of the breast model and receiving from opposite side diagonally. Using discrete cosine transform (DCT) of received signals, we have trained and tested the developed experimental Neural Network model. In 3D breast model, the achieved detection accuracy of tumor existence is around 100%, while the locating accuracy in terms of (x, y, z) position of a tumor within the breast reached approximately 89.2% and 86.6% in simulation and experimental works respectively. For classification, the permittivity and conductivity detection accuracy are 98.0% and 99.1% in simulation, and 98.6% and 99.5% in experimental works respectively. Tumor detection and type specification 3D may lead to successful clinical implementation followed by saving of precious human lives in the near future.

15598985
English
Article

author Alshehri S.A.; Khatun S.; Jantan A.B.; Abdullah R.S.A.R.; Mahmud R.; Awang Z.
spellingShingle Alshehri S.A.; Khatun S.; Jantan A.B.; Abdullah R.S.A.R.; Mahmud R.; Awang Z.
3D experimental detection and discrimination of malignant and benign breast tumor using NN-based UWB imaging system
author_facet Alshehri S.A.; Khatun S.; Jantan A.B.; Abdullah R.S.A.R.; Mahmud R.; Awang Z.
author_sort Alshehri S.A.; Khatun S.; Jantan A.B.; Abdullah R.S.A.R.; Mahmud R.; Awang Z.
title 3D experimental detection and discrimination of malignant and benign breast tumor using NN-based UWB imaging system
title_short 3D experimental detection and discrimination of malignant and benign breast tumor using NN-based UWB imaging system
title_full 3D experimental detection and discrimination of malignant and benign breast tumor using NN-based UWB imaging system
title_fullStr 3D experimental detection and discrimination of malignant and benign breast tumor using NN-based UWB imaging system
title_full_unstemmed 3D experimental detection and discrimination of malignant and benign breast tumor using NN-based UWB imaging system
title_sort 3D experimental detection and discrimination of malignant and benign breast tumor using NN-based UWB imaging system
publishDate 2011
container_title Progress in Electromagnetics Research
container_volume 116
container_issue
doi_str_mv 10.2528/PIER11022601
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-79958087925&doi=10.2528%2fPIER11022601&partnerID=40&md5=fda799d8e117509e8cb9e88b2a084c8d
description This paper presents both simulation and experimental study to detect and locate breast tumors along with their classification as malignant and/or benign in three dimensional (3D) breast model. The contrast between the dielectric properties of these two tumor types is the main key. These dielectric properties are mainly controlled by the water and blood content of tumors. For simulation, electromagnetic simulator software is used. The experiment is conducted using commercial Ultrawide-Band (UWB) transceivers, Neural Network (NN) based Pattern Recognition (PR) software for imaging and homogenous breast phantom. The 3D homogeneous breast phantom and tumors are fabricated using pure petroleum jelly and a mixture of wheat flour and water respectively. The simulation and experimental setups are performed by transmitting the UWB signals from one side of the breast model and receiving from opposite side diagonally. Using discrete cosine transform (DCT) of received signals, we have trained and tested the developed experimental Neural Network model. In 3D breast model, the achieved detection accuracy of tumor existence is around 100%, while the locating accuracy in terms of (x, y, z) position of a tumor within the breast reached approximately 89.2% and 86.6% in simulation and experimental works respectively. For classification, the permittivity and conductivity detection accuracy are 98.0% and 99.1% in simulation, and 98.6% and 99.5% in experimental works respectively. Tumor detection and type specification 3D may lead to successful clinical implementation followed by saving of precious human lives in the near future.
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