Enhancing Breast Cancer Detection: A Machine Learning Approach Using Multielectrode Bioimpedance

Abstract

Background:

Early detection of cancerous tumors is a critical factor in improving treatment outcomes. To address this need, this study explores a simple, effective, and cost-efficient method for early cancer detection by measuring the bioimpedance of living tissues. Bioimpedance-based methods hold significant promise for the early detection of cancerous tumors.

Materials and Methods:

The study begins by simulating the impedance behavior of the human breast under two conditions: healthy and containing cancerous tumors. The Cole–Cole model is used to simulate the dielectric properties of both breast and tumor tissues using finite element modeling. In the measurement phase, eight electrodes are evenly distributed around the breast model to ensure comprehensive data collection. Subsequently, a dataset is prepared encompassing three breast sizes (60, 70, and 80 mm) in both the healthy and tumor-afflicted states, with tumor sizes of 5, 8, and 10 mm radius. This dataset is utilized to develop machine learning models, including support vector machines (SVM), convolutional neural networks (CNN), and random forest (RF), for breast cancer detection.

Results:

The results of this study demonstrate the practicality of integrating machine learning techniques with multielectrode bioimpedance measurements to achieve precise and automated breast cancer detection. Notably, the RF model outperformed both SVM and CNN in terms of cancer detection accuracy.

Conclusions:

This study underscores the potential of bioimpedance-based methods, coupled with machine learning algorithms, for early cancer detection. The findings suggest that RF models hold promise for accurate and automated breast cancer detection, offering a valuable tool for improving patient outcomes.