AI-based hierarchical approach for optimizing breast cancer detection using MammoWave device

Abstract

Breast cancer is a global health concern, ranking as the second leading cause of death among women. Current screening methods, such as mammography, face limitations, particularly for women under 50 due to radiation concerns and frequency of examination restrictions. MammoWave, utilizing microwave signals (1 to 9 GHz), emerges as an innovative and safe technology for breast cancer detection. This paper focuses on the numerical data extracted from MammoWave, presenting a hierarchical approach to address challenges posed by a diverse dataset of over 1000 samples from two European hospitals. The proposed approach involves unsupervised clustering to classify data into two main groups, followed by binary classification within each group to distinguish healthy and non-healthy cases. Careful consideration is given to feature extraction methods and classifiers at each step. The unique influence of sub-bands within the 1 to 9 GHz range on the diagnosis model is observed, leading to the selection of suitable sub-bands, feature extraction methods, and classification models. An optimization algorithm and a defined cost function are employed to achieve high and balanced sensitivity, specificity, and accuracy values. Experimental results showcase a promising overall balanced performance of around 70 %, representing a significant milestone in breast cancer detection using microwave imaging. MammoWave, with its novel approach, provides a solution that overcomes age and frequency of examination related limitations associated with existing screening methods, contributing to enhanced breast health monitoring for a broader population.