In this work, a novel Ultra-wideband-Multi-Input-Multi-Output Antenna Sensor (UMAS) probe is designed for the detection of the malignant cells in the breast. The Sensor probe has four radiating elements and it is operated within the 2.8 GHz to 20 GHz ultra-wide band range. Isolation between the radiating element is more than 20 dB. Further, three kinds of the breast phantoms (i.e. normal phantom, phantom with single and multiple tumors) are fabricated using tissue mimicking material. The electrical characteristics of the malignant cells are different from non-malignant cells of the breast. The S-parameter and Specific Absorption Rate (SAR) analysis are best approaches to detect the malignant cells in the breast. The UMAS sensing probe is embedded on the phantoms and S-parameters of the probe are recorded from the Vector Network Analyzer (VNA). Measured S-parameters of the probe for normal and malignant phantoms are differ from each other. The statistical machine learning concept of Principal Component Analysis (PCA) is also applied on the measured S-Parameters. Which exhibits clear detection of normal and malignant breast phantoms. Further verification is done by using Simulation based specific absorption rate (SAR) study of the phantom models for tumor detection. The obtained maximum SAR results are well differentiating the normal phantom.
Experimental Investigation of the Breast Phantom for Tumor Detection Using Ultra-Wide Band-MIMO Antenna Sensor (UMAS) Probe
Lay Ekuakille A.
2020-01-01
Abstract
In this work, a novel Ultra-wideband-Multi-Input-Multi-Output Antenna Sensor (UMAS) probe is designed for the detection of the malignant cells in the breast. The Sensor probe has four radiating elements and it is operated within the 2.8 GHz to 20 GHz ultra-wide band range. Isolation between the radiating element is more than 20 dB. Further, three kinds of the breast phantoms (i.e. normal phantom, phantom with single and multiple tumors) are fabricated using tissue mimicking material. The electrical characteristics of the malignant cells are different from non-malignant cells of the breast. The S-parameter and Specific Absorption Rate (SAR) analysis are best approaches to detect the malignant cells in the breast. The UMAS sensing probe is embedded on the phantoms and S-parameters of the probe are recorded from the Vector Network Analyzer (VNA). Measured S-parameters of the probe for normal and malignant phantoms are differ from each other. The statistical machine learning concept of Principal Component Analysis (PCA) is also applied on the measured S-Parameters. Which exhibits clear detection of normal and malignant breast phantoms. Further verification is done by using Simulation based specific absorption rate (SAR) study of the phantom models for tumor detection. The obtained maximum SAR results are well differentiating the normal phantom.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
