Frequency Selection to Improve the Performance of Microwave Breast Cancer Detecting Support Vector Model by Using Genetic Algorithm

This paper presents an innovative paradigm for breast cancer detection by leveraging a Support Vector Machine (SVM) based model fueled with numerical data obtained from the cutting-edge MammoWave device. Operating in the microwave spectrum between 1 to 9 GHz and boasting a 5 MHz sampling rate, MammoWave emerges as a groundbreaking solution, specifically addressing the limitations posed by conventional methods, particularly for women under 50. This technological advancement opens a promising avenue for more frequent and precise breast health monitoring. To enhance the efficacy of the SVM model, our research introduces a metaheuristic-based methodology, strategically navigating the selection of frequencies crucial for breast cancer detection within the MammoWave dataset. Overcoming the challenge of judicious frequency selection, our approach employs wrapper methods in metaheuristic algorithms. These algorithms iterate through subsets of frequencies, guided by the SVM model's performance, culminating in the identification of the optimal frequency subset that significantly refines precision in breast cancer detection. Moreover, a novel cost function is proposed to strike a balanced trade-off between sensitivity and specificity, ensuring an acceptable accuracy rate. The results exhibit a noteworthy 10% increase in specificity, a milestone achievement for the MammoWave device, yielding an overall detection rate of approximately 62%. This research underscores the potential of seamlessly integrating metaheuristic algorithms into frequency selection, thereby contributing significantly to the ongoing refinement of MammoWave's capabilities in breast cancer detection.