This work presents the retrospective implementation of several supervised machine learning approaches on the microwave data obtained by MammoWave device in the framework of a clinical trial.
Microwave breast imaging is being investigated by research groups worldwide for its promising applications in early cancer detection, overcoming key limitations of conventional imaging systems. In this framework, artificial intelligence may play an important role to enhance the performances of new systems, based on this novel technology, for breast cancer detection. Research is being carried out to demonstrate the potential of implementing machine learning tools that have already been investigated for conventional mammography and MRI. This work presents the retrospective implementation of several supervised machine learning approaches on the microwave data obtained by MammoWave device in the framework of a clinical trial. Two different approaches are explored and explained in detail: the application of artificial intelligence directly on the MammoWave raw data and on dedicated features extracted from microwave images. Both approaches lead to promising results with high (>80%) and quite balanced specificity and sensitivity.
