Machine Learning-Based Frequency Selection to Improve Breast Cancer Detection in MammoWave Device

MammoWave, a novel microwave breast imaging device, employs a frequency range spanning from 1 to 9 GHz to acquire dielectric properties information from the breast. This study conducts a comprehensive analysis of various sub-bands to identify those or their combinations that enhance the efficacy of cancer detection. The findings reveal that leveraging the combined information from the 5-6, 7-8, and 8–9 GHz sub-bands yields notable improvements. These results underscore the potential of strategically utilizing specific frequency sub-bands for enhanced performance in breast cancer detection using MammoWave.