Complete Breast Cancer Detection and Monitoring System by Using Microwave Textile Based Antenna Sensors

• Published in: Biosensors (Basel) Vol. 13; no. 1; p. 87
• Main Authors: Elsheakh, Dalia N., Mohamed, Rawda A., Fahmy, Omar M., Ezzat, Khaled, Eldamak, Angie R.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04.01.2023; MDPI
• Subjects: Accuracy; Algorithms; Antennas; Breast; Breast cancer; Breast Neoplasms - diagnosis; Classification; Computer Simulation; coplanar waveguide monopole antenna specific absorption rate (SAR); Coplanar waveguides; Cotton; Dielectric properties; Efficiency; Female; Humans; Learning algorithms; Machine learning; machine-learning algorithms (MLA); Magnetic resonance imaging; Mammography; Medical screening; Microwaves; Monitoring; Monitoring systems; Radiation; Screen printing; Sensors; smart bra; Substrates; textile antenna sensor; Textiles; Tumors; Ultrasonic imaging; wearable; Womens health; Egypt; coplanar waveguide monopole antenna specific absorption rate (SAR); wearable; ultra-wide bandwidth (UWB); breast cancer; machine-learning algorithms (MLA); textile antenna sensor; smart bra
• ISSN: 2079-6374; 2079-6374
• DOI: 10.3390/bios13010087

This paper presents the development of a new complete wearable system for detecting breast tumors based on fully textile antenna-based sensors. The proposed sensor is compact and fully made of textiles so that it fits conformably and comfortably on the breasts with dimensions of 24 × 45 × 0.17 mm3 on a cotton substrate. The proposed antenna sensor is fed with a coplanar waveguide feed for easy integration with other systems. It realizes impedance bandwidth from 1.6 GHz up to 10 GHz at |S11| ≤ −6 dB (VSWR ≤ 3) and from 1.8 to 2.4 GHz and from 4 up to 10 GHz at |S11| ≤ −10 dB (VSWR ≤ 2). The proposed sensor acquires a low specific absorption rate (SAR) of 0.55 W/kg and 0.25 W/kg at 1g and 10 g, respectively, at 25 dBm power level over the operating band. Furthermore, the proposed system utilizes machine-learning algorithms (MLA) to differentiate between malignant tumor and benign breast tissues. Simulation examples have been recorded to verify and validate machine-learning algorithms in detecting tumors at different sizes of 10 mm and 20 mm, respectively. The classification accuracy reached 100% on the tested dataset when considering |S21| parameter features. The proposed system is vision as a “Smart Bra” that is capable of providing an easy interface for women who require continuous breast monitoring in the comfort of their homes.