Physics-informed neural network for fast prediction of temperature distributions in cancerous breasts as a potential efficient portable AI-based diagnostic tool
Mukhmetov O. Zhao Y. Mashekova A. Zarikas V. Ng E.Y.K. Aidossov N.
December 2023Elsevier Ireland Ltd
Computer Methods and Programs in Biomedicine
2023#242
This work presents the development of a novel Physics-Informed Neural Network (PINN) method for fast forward simulation of heat transfer through cancerous breast models. The proposed PINN method combines deep learning and physical principles to predict the temperature distributions in breast tissues and identify potential abnormal regions indicating the presence of tumors. The PINN model is normally trained by physics in terms of the residuals of the heat transfer equation, as well as boundary conditions with and without datasets of surface thermal imaging data concerning cancerous breast tissues, which can be used for future inverse thermal modeling to calculate tumor sizes and locations. The model is validated by comparing its predictions with those obtained by traditional Finite Element Analysis (FEA) for various cases. The comparison validates the PINN model as an accurate and fast method for thermal modeling and breast cancer diagnostic tool as the PINN simulation is found to be around 12 times faster than its FEM counterpart. The utilization of deep learning and physical principles in a diagnostic tool provides a non-invasive and safer alternative for breast self-examination compared to traditional methods such as mammography. These findings hold promise for the ongoing development of a new portable Artificial Intelligence (AI) tool for the early detection of breast cancer in breast self-examination as promoted by WHO, which is crucial for reducing mortality rates of breast cancer in the world.
Breast cancer , Finite Element Analysis , Physics-informed neural network , Thermography
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School of Engineering and Digital Sciences, Nazarbayev University, Astana, 010000, Kazakhstan
Department of Mathematics, University of Thessaly, Volos, Greece
Mathematical Sciences Research Laboratory (MSRL), Lamia, 35100, Greece
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore
School of Engineering and Digital Sciences
Department of Mathematics
Mathematical Sciences Research Laboratory (MSRL)
School of Mechanical and Aerospace Engineering
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