Regional Identification of Breast Tumors Using Multichannel Bioimpedance Spectroscopy

A multichannel bioimpedance spectroscopy (MC-BIS) method is proposed for regional identification of breast tumors. First, the sensor is partitioned to scan the breast across nine subregions, and an empty-field calibration algorithm is applied to normalize the impedance spectra. Next, numerical simulations are conducted to investigate the relationship between the electrical characteristics of unifocal tumors and their regional distribution. Subsequently, the performance of three classifiers—support vector machine (SVM), random forest (RF), and feedforward neural network (FNN)—is evaluated for bifocal tumor localization. The simulation results indicate significant differences in the impedance characteristics between tumor regions and normal tissue regions ( ${P} \lt 0.001$ ). When a tumor is present in a subregion, the corresponding imaginary part relaxation impedance ${Z} _{\text {imag-relax}}$ exceeds $2.004~\Omega $ . For bifocal breast tumor localization, the FNN classifier achieved the best performance, with a classification accuracy of 95.46% through fivefold cross-validation. To validate the simulation results, biological tissues with distinct electrical properties were selected to simulate tumor and normal tissue. The experimental accuracy reached 86.94%. The MC-BIS method enables rapid and accurate localization of tumor regions, providing a new technological approach for early screening and diagnosis of breast cancer.