Machine and deep learning applied to medical microwave imaging: a scoping review from reconstruction to...

Microwave imaging (MWI) is a promising modality due to its non-invasive nature and lower cost compared to other medical imaging techniques. These characteristics make it a potential alternative to traditional imaging techniques. It has various medical applications, particularly explored in breast and brain imaging. Machine learning (ML) has also been increasingly used for medical applications. This paper provides a scoping review of the role of ML in MWI, focusing on two key areas: image reconstruction and classification. The reconstruction section discusses various ML algorithms used to enhance image quality, highlighting methods such as convolutional neural network and support vector machine. The classification section delves into the application of ML for distinguishing between different tissue types, including applications in breast cancer detection and neurological disorder classification. By analyzing the latest studies and methodologies, this review addresses the current state of ML-enhanced MWI and sheds light on its potential for clinical applications.