پديدآورندگان :
Yousefnezhad Mohsen m.yousefnezhad@shirazu.ac.ir Department of Mathematics, Faculty of Sciences, Shiraz University, Fars, Iran. , Sabayemoghadam Avesta Department of Mathematics, Faculty of Sciences, Shiraz University, Fars, Iran. , Babaali Farzane Department of Mathematics, Faculty of Sciences, Shiraz University, Fars, Iran.
كليدواژه :
brain tumor , reaction diffusion , deep learning , physics , informed neural network
چكيده فارسي :
Brain tumors are complex and heterogeneous, making accurate modeling and prediction of their growth challenging. In this paper, we study a deep learning approach so called physics-informed neural network (PINN) for personalized brain tumor modeling that incorporates both patient-specific data and the underlying physics of tumor growth. We demonstrate the effectiveness of our approach on a synthetic dataset of brain tumor in one-dimension.
