Automated Breast Tissue Classification Through Machine Learning Using Dielectric Data

In recent years, new technologies focused on dielectric principles have been developed for medical applications. Conductivity and permittivity of biological tissues have been described to vary among benign and malignant tissues, so many efforts are being made to implement new systems based on safe low-power microwaves able to capture these inhomogeneities for medical imaging. However, such conductivity and permittivity parameters are being investigated for several different applications. The dielectric characterization of tissues in vivo during surgeries or via excised tissue may offer clinicians new tools for optimizing hospital routines in the diagnostic pathway. This work presents the application of several Machine Learning (ML) approaches to dielectric data gathered from excised breast tissues using a novel open-ended coaxial probe.