Antenna Array Optimization via Deep Learning for Breast Cancer Microwave Hyperthermia Application: Preliminary Results

Gulsah Altintas; Halimcan Yasar; Ibrahim Enes Uslu; Yusuf Demirel; Sulayman Joof; Mehmet Nuri Akinci; Tuba Yilmaz; Ibrahim Akduman

doi:10.1109/AP-S/USNC-URSI47032.2022.9887012

Antenna Array Optimization via Deep Learning for Breast Cancer Microwave Hyperthermia Application: Preliminary Results

Gulsah Altintas, Halimcan Yasar, Ibrahim Enes Uslu, Yusuf Demirel, Sulayman Joof, Mehmet Nuri Akinci, Tuba Yilmaz, Ibrahim Akduman

Department of Electronics and Communication Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Microwave hyperthermia (MH) requires the effective calibration of the antenna for selective focusing of the microwave energy at the target region with a nominal effect on the surrounding tissue. Many different antenna calibration methods such as optimization techniques and lookup tables have been proposed. In this paper, we present the preliminary results of a CNN based phase and power optimization approach. To create the necessary dataset, we used the superposition method to combine the information from the individual antennas. The results of the CNN model are compared with lookup table results. The proposed approach is promising as it shows less hot spots in heating potential distributions.

Original language	English
Title of host publication	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	697-698
Number of pages	2
ISBN (Electronic)	9781665496582
DOIs	https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887012
Publication status	Published - 2022
Event	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Denver, United States Duration: 10 Jul 2022 → 15 Jul 2022

Publication series

Name	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings

Conference

Conference	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022
Country/Territory	United States
City	Denver
Period	10/07/22 → 15/07/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Funding

This work has received funding from Scientific and Technological Research Council of Turkey under grant agreement 118S074, and COST Action grant agreement CA17115.

Funders	Funder number
European Cooperation in Science and Technology	CA17115
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu	118S074

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/AP-S/USNC-URSI47032.2022.9887012

Cite this

Altintas, G., Yasar, H., Uslu, I. E., Demirel, Y., Joof, S., Akinci, M. N., Yilmaz, T., & Akduman, I. (2022). Antenna Array Optimization via Deep Learning for Breast Cancer Microwave Hyperthermia Application: Preliminary Results. In 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings (pp. 697-698). (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887012

Altintas, Gulsah ; Yasar, Halimcan ; Uslu, Ibrahim Enes et al. / Antenna Array Optimization via Deep Learning for Breast Cancer Microwave Hyperthermia Application : Preliminary Results. 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 697-698 (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings).

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abstract = "Microwave hyperthermia (MH) requires the effective calibration of the antenna for selective focusing of the microwave energy at the target region with a nominal effect on the surrounding tissue. Many different antenna calibration methods such as optimization techniques and lookup tables have been proposed. In this paper, we present the preliminary results of a CNN based phase and power optimization approach. To create the necessary dataset, we used the superposition method to combine the information from the individual antennas. The results of the CNN model are compared with lookup table results. The proposed approach is promising as it shows less hot spots in heating potential distributions.",

author = "Gulsah Altintas and Halimcan Yasar and Uslu, {Ibrahim Enes} and Yusuf Demirel and Sulayman Joof and Akinci, {Mehmet Nuri} and Tuba Yilmaz and Ibrahim Akduman",

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Altintas, G, Yasar, H, Uslu, IE, Demirel, Y, Joof, S, Akinci, MN , Yilmaz, T & Akduman, I 2022, Antenna Array Optimization via Deep Learning for Breast Cancer Microwave Hyperthermia Application: Preliminary Results. in 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 697-698, 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022, Denver, United States, 10/07/22. https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887012

Antenna Array Optimization via Deep Learning for Breast Cancer Microwave Hyperthermia Application: Preliminary Results. / Altintas, Gulsah; Yasar, Halimcan; Uslu, Ibrahim Enes et al.
2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 697-698 (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Microwave hyperthermia (MH) requires the effective calibration of the antenna for selective focusing of the microwave energy at the target region with a nominal effect on the surrounding tissue. Many different antenna calibration methods such as optimization techniques and lookup tables have been proposed. In this paper, we present the preliminary results of a CNN based phase and power optimization approach. To create the necessary dataset, we used the superposition method to combine the information from the individual antennas. The results of the CNN model are compared with lookup table results. The proposed approach is promising as it shows less hot spots in heating potential distributions.

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Altintas G, Yasar H, Uslu IE, Demirel Y, Joof S, Akinci MN et al. Antenna Array Optimization via Deep Learning for Breast Cancer Microwave Hyperthermia Application: Preliminary Results. In 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 697-698. (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings). doi: 10.1109/AP-S/USNC-URSI47032.2022.9887012

Antenna Array Optimization via Deep Learning for Breast Cancer Microwave Hyperthermia Application: Preliminary Results

Abstract

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Bibliographical note

Funding

UN SDGs

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