Machine Learning Methods for Microwave Imaging in Cancer Detection

This study discusses a new technique based on the Born iterative method, with a quadratic programming approach, which uses convolutional neural networks to solve microwave inverse scattering problems for breast cancer detection. The ultimate goal is to reconstruct the permittivity of breast tissues from the measured scattering values, which poses a great challenge because these tissues are characterized by strong dielectric scatterers. To evaluate the performance of the proposed method, simulations were performed for two breast phantom models with tumors in different positions. The results obtained are very good and show that the application of Convolutional Neural Networks helps to significantly reduce the reconstruction error.