Fast and Accurate CNN-Based Machine Learning Approach for Microwave Medical Imaging in Cancer Detection

The application and comparison of U-Net convolutional neural network architectures is proposed in this work to guarantee a fast and accurate convergence of inverse scattering problems solved by Born iterative method, even in the presence of strong scatterers. Starting from a preliminary configuration proposed by the authors in some recent papers, two variants are introduced and discussed to significantly reduce the computational cost, while guaranteeing convergence with very high accuracy in the dielectric profiles reconstruction when considering strong scatterers, such as tumors, thus working as a regularization process to mitigate the induced non-linearity. As a further enhancement, a novel approach is introduced which integrates U-Net and Resnet models to realize a segmentation process, thus leading to the effective feature extraction and the accurate identification of anomalies within healthy tissue. Numerical assessments on a variety of breast models including abnormal lesions are discussed to successfully validate the proposed machine learning approach, through the adoption of properly defined evaluation metrics.