Near-Field Microwave Scattering Formulation by a Deep Learning Method

Wenyi Shao, Beibei Zhou

Research output: Contribution to journalArticlepeer-review


A deep learning (DL) method is applied to modeling electromagnetic (EM) scattering for microwave breast imaging (MBI). The neural network (NN) accepts 2-D dielectric breast maps at 3 GHz and produces scattered-field data on an antenna array composed of 24 transmitters and 24 receivers. The NN was trained by 18000 synthetic digital breast phantoms generated by generative adversarial network (GAN), and the scattered-field data pre-calculated by method of moments (MOM). Validation was performed by comparing the 2000 NN-produced datasets isolated from the training data with the data computed by MOM. Finally, data generated by NN and MOM were used for image reconstruction. The reconstruction demonstrated that errors caused by NN would not significantly affect the image result. But, the computational speed of NN was nearly 104 times faster than the MOM, indicating that DL has the potential to be considered as a fast tool for EM scattering computation.

Original languageEnglish (US)
Pages (from-to)5077-5084
Number of pages8
JournalIEEE Transactions on Microwave Theory and Techniques
Issue number11
StatePublished - Nov 1 2022


  • Computational electromagnetics (EMs)
  • convolutional neural network (NN)
  • deep learning (DL)
  • microwave imaging

ASJC Scopus subject areas

  • Radiation
  • Condensed Matter Physics
  • Electrical and Electronic Engineering


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