Data-Driven Modeling and Optimization for Non-Invasive Microwave Hyperthermia

Zhiyong Chen

doi:10.47297/taposatWSP2633-456914.20250612

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Data-Driven Modeling and Optimization for Non-Invasive Microwave Hyperthermia

6卷第12期

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Data-Driven Modeling and Optimization for Non-Invasive Microwave Hyperthermia
Data-Driven Modeling and Optimization for Non-Invasive Microwave Hyperthermia
University of Shanghai for Science and Technology, Shanghai, 200093, China
2025年6卷第12期页码：67-71
纸质出版：2025-12-30
DOI：10.47297/taposatWSP2633-456914.20250612
Accepted：

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Zhiyong Chen. (2025). Data-Driven Modeling and Optimization for Non-Invasive Microwave Hyperthermia. 科技理论与实践（英文版）, 6(12), 67-71.

Chen Zhiyong.Data-Driven Modeling and Optimization for Non-Invasive Microwave Hyperthermia[J].Theory and Practice of Science and Technology,2025,06(12):67-71.
Zhiyong Chen. (2025). Data-Driven Modeling and Optimization for Non-Invasive Microwave Hyperthermia. 科技理论与实践（英文版）, 6(12), 67-71. DOI： 10.47297/taposatWSP2633-456914.20250612.

Chen Zhiyong.Data-Driven Modeling and Optimization for Non-Invasive Microwave Hyperthermia[J].Theory and Practice of Science and Technology,2025,06(12):67-71. DOI： 10.47297/taposatWSP2633-456914.20250612.

摘要

Abstract

In this paper

the modeling for non-invasive microwave hyperthermia used in tumor treatment is introduced. By linearly simplifying the traditional Pennes model

we constructed two models to address the issue of unmeasurable internal temperature. The first model is the Output-Error(OE) model

which estimates the unmeasurable temperature inside the heated area based on measurable surface temperature. The second model is the Controlled Auto-Regressive(CAR) model

which describes the relationship between the microwave-driven signal and the estimated internal temperature. And they are identified respectively using the Particle Swarm Optimization (PSO) and the Mayfly Optimization Algorithm (MOA) intelligent optimization algorithms. Heating experiments were conducted on a biological platform

and the experimental results showed that the models under this modeling strategy have good accuracy and can provide good temperature estimations for microwave hyperthermia. Finally

experimental validation is conducted on a microwave hyperthermia platform. The proposed method exhibits excellent modeling accuracy

parameter convergence

and strong robustness

confirming its effectiveness for practical clinical medical applications.

关键词

Keywords

references

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