Experimental breast tumor detection using NN-based UWB imaging

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

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Bibliographic Details
Main Authors: Alshehri, S.A (Author), Awang, Z. (Author), Jantan, A.B (Author), Khatun, S. (Author), Mahmood, R. (Author), Raja Abdullah, R.S.A (Author)
Format: Article
Language:English
Published: Electromagnetics Academy 2011
Subjects:
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LEADER 02775nam a2200421Ia 4500
001 10.2528-PIER10110102
008 220112s2011 CNT 000 0 und d
020 |a 10704698 (ISSN) 
245 1 0 |a Experimental breast tumor detection using NN-based UWB imaging 
260 0 |b Electromagnetics Academy  |c 2011 
856 |z View Fulltext in Publisher  |u https://doi.org/10.2528/PIER10110102 
856 |z View in Scopus  |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-78751523626&doi=10.2528%2fPIER10110102&partnerID=40&md5=4216131166a09d9874bc803d465c02c9 
520 3 |a This paper presents a system with experimental complement to a simulation work for early breast tumor detection. The experiments 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 (homogeneous and heterogeneous) and tumor are constructed using available low cost materials and their mixtures with minimal e®ort. A specific glass is used as skin. All the materials and their mixtures are considered 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 detection and the practical usefulness of the developed system in near future. 
650 0 4 |a Breast phantom 
650 0 4 |a Breast tissues 
650 0 4 |a Breast tumor detection 
650 0 4 |a Dielectric materials 
650 0 4 |a Dielectric properties 
650 0 4 |a Discrete Cosine Transform(DCT) 
650 0 4 |a Discrete cosine transforms 
650 0 4 |a Heterogeneous tissues 
650 0 4 |a Histology 
650 0 4 |a Location detection 
650 0 4 |a Medical imaging 
650 0 4 |a Mixtures 
650 0 4 |a Neural network (nn) 
650 0 4 |a Pattern recognition 
650 0 4 |a Radio transceivers 
650 0 4 |a Received signals 
650 0 4 |a Tissue 
650 0 4 |a Tumors 
700 1 0 |a Alshehri, S.A.  |e author 
700 1 0 |a Awang, Z.  |e author 
700 1 0 |a Jantan, A.B.  |e author 
700 1 0 |a Khatun, S.  |e author 
700 1 0 |a Mahmood, R.  |e author 
700 1 0 |a Raja Abdullah, R.S.A.  |e author 
773 |t Progress in Electromagnetics Research