Enhancing the accuracy of skin cancer cell localization using sum and difference modes of microwave resonators

This paper introduces a novel sensor system for early skin cancer detection based on measuring skin permittivity (εr) using a microwave-based coupled resonator structure. The proposed method enables the accurate extraction of both real and imaginary components of permittivity. Unlike approaches relying on absolute permittivity values, which may vary due to factors such as age, gender, and individual differences, this system employs a comparative method that analyzes the difference between suspicious and healthy skin areas. To realize this comparative sensing, a 180° ultra-wideband hybrid coupler was integrated into the original single-resonator sensor, significantly enhancing overall system performance and enabling dual-mode operation. Two sensing modes are implemented: resonance frequency comparison and amplitude comparison. For the first time in this context, microwave frequencies (3–10.5 GHz) are employed, offering deeper electric field penetration into skin tissue compared to millimeter-wave or terahertz techniques. This allows scanning the entire skin thickness, improving diagnostic accuracy. Experimental results demonstrate that the comparative approach increases sensitivity by 37.24% compared to the conventional single-resonator design. The sensor exhibits a sensitivity of 85 MHz/RIU, a quality factor of 465, and a figure of merit (FOM) of 39,525 RIU−1. These performance metrics highlight the system’s potential for precise detection of subtle permittivity differences associated with early-stage cancerous tissues. The integration of microwave resonators with the 180° ultra-wideband hybrid coupler enables non-invasive, label-free, and highly sensitive skin cancer diagnostics. A novel microwave sensor enables early skin cancer detection by comparing healthy and suspicious areas. Dual-mode design with a 180° hybrid coupler boosts sensitivity and allows deep skin-layer assessment. Provides a non-invasive, label-free approach for detecting subtle tissue changes at early cancer stages.