Tumor Detection and Characterization Using Microwave Imaging Technique—An Experimental Calibration Approach

Microwave imaging (MWI) is a non-invasive technique for visualizing the anomalies of biological tissues. The imaging process is accomplished by comparing the electrical parameters of healthy tissues and malignant tissues. This work introduces a microwave imaging system for tumor detection in breast tissue. The experiment is performed in a homogeneous background medium, where a high dielectric contrast material is used to mimic the tumor. The proposed imaging system is experimentally evaluated for multiple tumor locations and sizes using a horn antenna. Reflection coefficients obtained from the monostatic configuration of the horn antenna are used for image reconstruction. The evaluation metrics, such as localization error, absolute area error, DICE score, Intersection over Union (IoU), precision, accuracy, sensitivity and specificity, are computed from the reconstructed image. A modified version of the beamforming algorithm improves the quality of reconstructed images by providing a minimum accuracy of 96% for all test cases, with an evaluation time of less than 48 s. The proposed methodology shows promising results under a controlled environment and can be implemented for clinical applications after adequate biological studies. This methodology can be used to calibrate any antenna system or phantom, as it has high contrast in conductivity, leading to better imaging. The present study contributes to Sustainable Development Goal (SDG) 3 by ensuring healthy lives and promoting wellbeing for all ages.

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Publication Details

Journal
Sensors
Published
2026-02-04
DOI
https://doi.org/10.3390/s26031014
Primary Topic
Microwave Imaging and Scattering Analysis
Type
article
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article

Tumor Detection and Characterization Using Microwave Imaging Technique—An Experimental Calibration Approach

Anand Nair, Massimo Donelli, Sreedevi K. Menon, Naveen Krishnan Radhakrishna Pillai et al.
Sensors
Microwave Imaging and Scattering Analysis
article

Tumor Detection and Characterization Using Microwave Imaging Technique—An Experimental Calibration Approach

Anand Nair, Massimo Donelli, Sreedevi K. Menon, Naveen Krishnan Radhakrishna Pillai, Suraksha Rajagopalan
article en

Abstract

Microwave imaging (MWI) is a non-invasive technique for visualizing the anomalies of biological tissues. The imaging process is accomplished by comparing the electrical parameters of healthy tissues and malignant tissues. This work introduces a microwave imaging system for tumor detection in breast tissue. The experiment is performed in a homogeneous background medium, where a high dielectric contrast material is used to mimic the tumor. The proposed imaging system is experimentally evaluated for multiple tumor locations and sizes using a horn antenna. Reflection coefficients obtained from the monostatic configuration of the horn antenna are used for image reconstruction. The evaluation metrics, such as localization error, absolute area error, DICE score, Intersection over Union (IoU), precision, accuracy, sensitivity and specificity, are computed from the reconstructed image. A modified version of the beamforming algorithm improves the quality of reconstructed images by providing a minimum accuracy of 96% for all test cases, with an evaluation time of less than 48 s. The proposed methodology shows promising results under a controlled environment and can be implemented for clinical applications after adequate biological studies. This methodology can be used to calibrate any antenna system or phantom, as it has high contrast in conductivity, leading to better imaging. The present study contributes to Sustainable Development Goal (SDG) 3 by ensuring healthy lives and promoting wellbeing for all ages.

SensorsVol. 26(3)
University of Trento (IT), Amrita Vishwa Vidyapeetham (IN)
Openalex Percentile: Top 68%
Microwave Imaging and Scattering Analysis
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Tumor Detection and Characterization Using Microwave Imaging Technique—An Experimental Calibration Approach — Anand Nair, Massimo Donelli, et al. · Sensors (2026) | TGRS Research Map | TGRS