The paper “Machine Learning Classification of Axillary Lymph Nodes Using Microwave Signals” was recently published in the journal Sensors. This study emerged from the PhD research of Daniela Godinho, supervised at the time by Raquel Conceição, and explores the use of machine learning algorithms to support the assessment of axillary lymph nodes, which are key structures in breast cancer staging.
In this work, the researchers demonstrated, for the first time, the feasibility of classifying healthy and metastatic lymph nodes directly from microwave signals, without the need for image reconstruction. To achieve this, 80 three-dimensional lymph node models were developed based on anatomical characteristics described in the scientific literature, enabling the simulation of different clinical scenarios of increasing complexity.
In addition to being the first study dedicated to the classification of axillary lymph nodes using microwave signals, the research addresses challenges specific to the axillary region, particularly the limited field of view available for signal acquisition, a constraint that is not present in other microwave imaging applications, such as breast or brain imaging.
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