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Monostatic Radar Based Ultra-Wideband Microwave Imaging System Featuring a Miniature Fork Shaped Microstrip Patch Antenna with a Reduced DGS for Early Breast Tumor Detection

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Intelligent Communication, Control and Devices

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 989))

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Abstract

This paper presents the usage of a fork-shaped microstrip patch antenna for early breast tumor diagnosis based on significant contrast in dielectric properties between healthy and malignant tissue. The proposed antenna is designed with a 50Ω microstrip feed line and has compact dimensions of 24 × 28 × 1.64 mm3 in CST MWS V’18. The antenna structure has a slotted rectangular patch with a reduced ground to achieve high gain, miniaturization, and UWB characteristics. A 3D spherical breast phantom is modeled with 3 mm tumor radius and simulated using CST MWS V’18 with different dielectric properties of skin, fatty tissue, and tumor. The breast phantom is oriented parallel to the broadside radiating surface of the antenna and the backscattered signals are recorded when the phantom is illuminated by the microwave signals for with and without tumor. The simulated results show that more reflections, lesser specific absorption rate and more conduction current density is obtained in the presence of tumor as compared to a nonmalignant case thereby making the detection of the tumor in the breast feasible.

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Acknowledgments

The authors are thankful to University Grants Commission, New Delhi, India has provided a Fund grant of 15.8 lakhs under the major project scheme for setting up the antenna testing facility at Thapar University, Patiala.

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Correspondence to Arashpreet Kaur .

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Kaur, A., Kaur, A. (2020). Monostatic Radar Based Ultra-Wideband Microwave Imaging System Featuring a Miniature Fork Shaped Microstrip Patch Antenna with a Reduced DGS for Early Breast Tumor Detection. In: Choudhury, S., Mishra, R., Mishra, R., Kumar, A. (eds) Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol 989. Springer, Singapore. https://doi.org/10.1007/978-981-13-8618-3_13

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