Non-Invasive Breast Cancer Detection Using Physics Informed Neural Networks with Thermal Imaging and 3D Patient-Specific Breast Models
Mukhmetov O. Zhao Y. Mashekova A. Wei D. Zarikas V. Ng E.Y.K. Salama A. Shapatova M. Aidossov N.
December 2025Engineered Science Publisher
Engineered Science
2025#38
This study presents a novel non-invasive approach for breast cancer detection and tumor localization by developing an optimized Physics-Informed Neural Network (PINN) model integrated with infrared (IR) thermal imaging and 3D physical breast modeling. The proposed method leverages thermal data from an IR camera and anatomical information from a 3D scanner to train a PINN, incorporating the bioheat equation to perform both forward and inverse predictions. The PINN is uniquely optimized to estimate bio-physical parameters, such as tumor radius and depth, enabling accurate tumor diagnosis. Validation against Finite Element Method (FEM) simulations from ANSYS demonstrates that the PINN model achieves high accuracy, with error margins as low as 0.70 for radius and 1.38 for depth after training optimizations. Compared to traditional FEM solvers, the PINN model offers 10 times faster simulation post training, highlighting its computational efficiency. This work underscores the potential of PINNs as a promising tool for non-invasive breast cancer diagnostics, combining physical constraints, anatomical accuracy, and machine learning for enhanced tumor detection and localization.
3D breast modeling , Breast cancer detection , Finite element method (FEM) , Infrared thermal imaging , Physics-informed neural networks (PINNs)
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Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, 010000, Kazakhstan
Department of Mathematics, School of Sciences and Humanities, Nazarbayev University, Astana, 010000, Kazakhstan
Department of Mathematics, University of Thessaly, Thessaly, Volos, Greece
Mathematical Sciences Research Laboratory (MSRL), Lamia, 35100, Greece
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore
Medical Center Hospital of the President’s affairs Administration of the Republic of Kazakhstan, E495 build No 2, Astana, Kazakhstan
School of Intelligent Systems, Astana IT University, E495 build No 2, Astana, 010000, Kazakhstan
Department of Mechanical and Aerospace Engineering
Department of Mathematics
Department of Mathematics
Mathematical Sciences Research Laboratory (MSRL)
School of Mechanical and Aerospace Engineering
Medical Center Hospital of the President’s affairs Administration of the Republic of Kazakhstan
School of Intelligent Systems
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