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PIER PIER B PIER C PIER M PIER Letters
2010-12-22
Experimental Breast Tumor Detection Using Nn-Based UWB Imaging
By
Saleh Ali AlShehri,

    Dr. Saleh Ali AlShehri

    Department of Computer and Communication Systems Engineering

    Universiti Putra Malaysia

    Malaysia

    Homepage

Sabira Khatun,

    Prof. Sabira Khatun

    Department of Computer and Communication Systems Engineering

    Universiti Putra Malaysia

    Malaysia

    Homepage

Adznan B. Jantan,

    Dr. Adznan B. Jantan

    Department of Computer and Communication Systems Engineering

    Universiti Putra Malaysia

    Malaysia

    Homepage

Raja Syamsul Azmir Raja Abdullah,

    Dr. Raja Syamsul Azmir Raja Abdullah

    Department of Computer and Communication System Engineering

    Universiti Putra Malaysia

    Malaysia

    Homepage

Rozi Mahmud,

    Dr. Rozi Mahmud

    Department of Imaging

    Universiti Putra Malaysia

    Malaysia

    Homepage

Zaiki Awang

    Dr. Zaiki Awang

    Universiti Teknologi Mara

    Malaysia

    Homepage

Progress In Electromagnetics Research, Vol. 111, 447-465, 2011
doi:10.2528/PIER10110102
Abstract
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 effort. 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.
Citation
Saleh Ali AlShehri, Sabira Khatun, Adznan B. Jantan, Raja Syamsul Azmir Raja Abdullah, Rozi Mahmud, and Zaiki Awang, "Experimental Breast Tumor Detection Using Nn-Based UWB Imaging," Progress In Electromagnetics Research, Vol. 111, 447-465, 2011.
doi:10.2528/PIER10110102
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