Influence of thermal contrast and limitations of a deep-learning based estimation of early-stage tumour parameters in different breast shapes using simulated passive and dynamic thermography

Date published

2025-04

Free to read from

2025-03-26

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Journal ISSN

Volume Title

Publisher

Elsevier

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Type

Article

ISSN

2451-9049

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Citation

Moraes MFB, Sfarra S, Fernandes H, Figueiredo AAA. (2025) Influence of thermal contrast and limitations of a deep-learning based estimation of early-stage tumour parameters in different breast shapes using simulated passive and dynamic thermography. Thermal Science and Engineering Progress, Volume 60, April 2025, Article number 103418

Abstract

To enhance diagnostic sensitivity compared to passive thermography, thermal stress can be applied to the breast surface with the temperatures being measured in the thermal recovery phase, a process called dynamic thermography. This study aims to evaluate the limitations of both passive and dynamic thermography in estimating early-stage tumour parameters across different breast shapes and how to improve the results. Three breast models with thermoregulation were solved numerically using COMSOL Multiphysics®. A neural network developed in PyTorch was used to estimate breast tumour location and size. The estimates obtained using each approach were compared, and the effects of thermal contrast, noise, and tumour depth range were analysed. Dynamic thermography provided the most accurate estimates compared to passive thermography, with mean error reductions that reached up to 33.25%. Additionally, the number of estimates with errors higher than 10% was up to 48.42% lower. Tumour radius showed the lowest noise threshold, providing the highest estimations errors. Adding deeper tumours to the datasets caused mean error increases of up to 51.27%. Thus, this work contributes by comparing both types of thermography, analysing thermal aspects of the temperature data that influences the neural network's estimation process, and suggesting alternatives to improve its accuracy.

Description

Software Description

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Github

Keywords

4012 Fluid Mechanics and Thermal Engineering, 40 Engineering, 4017 Mechanical Engineering, Cancer, Breast Cancer, Women's Health, Cancer, 4012 Fluid mechanics and thermal engineering, 4017 Mechanical engineering, Bio-heat transfer, Breast tumour, Dynamic thermography, Inverse problem, Neural network

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Attribution-NonCommercial 4.0 International

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Funder/s

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001. H.F. is grateful for the support provided by the Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brazil (CNPq) - Finance Code 312530/2023-4.