Abstract
This paper presents experimental study to distinguish between malignant and benign tumors in early breast cancer detection using Ultra Wide Band (UWB) imaging. The contrast between dielectric properties of these two tumor types is the main key. Mainly water contents control the dielectric properties. Breast phantom and tumor are fabricated using pure petroleum jelly and a mixture of wheat flour and water respectively. A complete system including Neural Network (NN) model is developed for experimental investigation. Received UWB signals through the tumor embedded breast phantom are fed into the NN model to train, test and determine the tumor type. The accuracy of the experimental data is about 98.6% and 99.5% for permittivity and conductivity respectively. This leads to determine tumor dielectric properties accurately followed by distinguish between malignant and benign tumors. As malignant tumors need immediate further medical action and removal, this findings could contribute to save precious file in near future.
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References
Li, X., Davis, S.K., Hagness, S.C., Weide, D.W., Veen, B.D.: Microwave imaging via space-time beam forming: experimental investigation of tumor detection in multilayer breast phantoms. IEEE Trans. Microwave Theory Techniques. 52, 1856–1865 (2004)
Alshehri, S.A., Khatun, S.: UWB imaging for breast cancer detection using neural networks. Progress In Electromagnetic Research C 7, 79–93 (2009)
Sha, L., Ward, E.R., Story, B.: A review of dielectric properties of normal and malignant breast tissue. In: Proceedings IEEE SoutheastCon, pp. 457–462 (April 5-7, 2002)
Lai, J.C., Soh, C.B., Gunawan, E., Low, K.S.: Homogeneous and heterogeneous breast phantom for ultra-wideband microwave imaging applications. Progress In Electromagnetic Research 100, 377–415 (2010)
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Lazebnik, M., et al.: A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries. Phys. Med. Biol. 52, 6093–6115 (2007)
Rangayyan, R.M., El-Faramawy, N.M., Leo Desautels, J.E., Alim, O.A.: Measures of acutance and shape for classification of breast tumor. IEEE Transactions on Medical Imaging 16, 799–810 (1997)
Davis, S.K., Van Veen, B.D., Hagness, S.C., Kelcz, F.: Breast Tumor characterization based on ultrawideband microwave backscatter. IEEE Transactions On Biomedical Engineering 55, 237–246 (2008)
Insana, M.F., Pellot-Barakat, C., Sridhar, M., Lindfors, K.K.: Viscoelastic imaging of breast tumor microenvironment with ultrasound. Journal of Mammary Gland Biology and Neoplasia 9, 393–404 (2004)
Bindu, G., Lonappan, A., Thomas, V., Ananadan, C.K., Mathew, K.T.: Active microwave imaging for breast cancer detection. Progress In Electromagnetic Research 58, 149–169 (2006)
Bindu, G., Mathew, K.T.: Characterization of benign and malignant breast tissues using 2-D microwave tomographic imaging. Microwave And Optical Technology Letters 49, 2341–2345 (2007)
Hagness, S.C., Taflove, A., Bridges, J.E.: Three dimensional FDTD analysis of a pulsed microwave confocal system for breast cancer detection design of an antenna-array element. IEEE Transactions on Antennas And Propagation 47, 783–791 (1999)
Alshehri, S.A., Khatun, S., Jantan, A., Raja Abdullah, R.S.A., Mahmood, R., Awang, Z.: Experimental Breast Tumor Detection Using NN-Based UWB Imaging. Progress In Electromagnetic Research 111, 447–465 (2011)
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Alshehri, S., Khatun, S., Jantan, A., Raja Abdullah, R.S.A., Mahmud, R., Awang, Z. (2011). Experimental Approximation of Breast Tissue Permittivity and Conductivity Using NN-Based UWB Imaging. In: Mohamad Zain, J., Wan Mohd, W.M.b., El-Qawasmeh, E. (eds) Software Engineering and Computer Systems. ICSECS 2011. Communications in Computer and Information Science, vol 179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22170-5_29
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DOI: https://doi.org/10.1007/978-3-642-22170-5_29
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