Abstract
Mammary carcinoma, breast cancer, is the most commonly diagnosed cancer type among women. Therefore, potential new technologies for the diagnosis and treatment of the disease are being investigated. One promising technique is microwave applications designed to exploit the inherent dielectric property discrepancy between the malignant and normal tissues. In theory, the anomalies can be characterized by simply measuring the dielectric properties. However, the current measurement technique is error-prone and a single measurement is not accurate enough to detect anomalies with high confidence. This work proposes to classify the rat mammary carcinoma, based on collected large-scale in vivo S11 measurements and corresponding tissue dielectric properties with a circular diffraction antenna. The tissues were classified with high accuracy in a reproducible way by leveraging a learning-based linear classifier. Moreover, the most discriminative S11 measurement was identified, and to our surprise, using the discriminative measurement along with a linear classifier an 86.92% accuracy was achieved. These findings suggest that a narrow band microwave circuitry can support the antenna enabling a low-cost automated microwave diagnostic system.
Original language | English |
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Article number | 349 |
Journal | Scientific Reports |
Volume | 12 |
Issue number | 1 |
DOIs | |
Publication status | Published - Dec 2022 |
Bibliographical note
Publisher Copyright:© 2022, The Author(s).
Funding
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 750346 and Scientific and Technological Research Council of Turkey under grant agreement 118S074.
Funders | Funder number |
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Horizon 2020 Framework Programme | |
H2020 Marie Skłodowska-Curie Actions | 750346 |
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu | 118S074 |
Horizon 2020 |