Detection Of Ovarian Cancer Using Bio-Impedance

Abstract

Ovarian cancer is a highly fatal gynecologic malignancy often diagnosed at advanced stages due to the absence of reliable early diagnostic techniques. This study introduces a non-invasive diagnostic approach based on bio-impedance analysis to detect ovarian cancer in its early stages. Bio-impedance quantifies the opposition of biological tissues to an externally applied alternating current, which varies with tissue composition, membrane capacitance, and intracellular resistance. Malignant tissues exhibit distinct impedance profiles compared to healthy counterparts due to altered cellular architecture and dielectric properties. A hardware system was developed using surface-mounted Ag/AgCl electrodes for multi-frequency impedance acquisition across the abdominal region. The captured impedance spectra were processed and evaluated using signal conditioning techniques and classified through supervised machine learning algorithms to differentiate pathological from normal states. Experimental validation on tissue-equivalent phantoms demonstrated consistent impedance deviations correlating with simulated malignancy. The proposed system offers potential as a point-of-care, low-cost diagnostic tool capable of early anomaly identification without the need for imaging or invasive procedures. Future work will focus on clinical trials, impedance mapping optimization, and real-time embedded system integration to enhance diagnostic precision and portability in remote and primary care environments.