Microwave Sensor Using RF Technology for the Non-Invasive Melanoma's Levels Diagnosis
Year: 2026
Type: conference-paper
Abstract: Emerging technologies have introduced innovative and promising approaches for the detection of skin cancer, particularly melanoma, the most aggressive and pernicious form. These methods aim to provide non-invasive diagnostic and prognostic capabilities, enabling real-time identification of disease progression from early stages to metastatic forms. The ultimate objective is to facilitate earlier therapeutic intervention and improve patient survival outcomes. Among these technologies, radiofrequency (RF) techniques demonstrate significant potential in biomedical engineering. By exploiting electromagnetic waves to transmit or analyze signals, RF methods enable rapid, sensitive, and Non-Invasive tissue characterization. They support real-time diagnostics and the detection of biological anomalies, such as distinctions between healthy and malignant tissues, without requiring invasive procedures like biopsy. RF applications extend to medical imaging, physiological monitoring, and therapeutic interventions such as hyperthermia, positioning RF as an innovative tool for early detection and monitoring of melanoma and other skin cancers. In this study, we propose an application of microwave sensors based on a rectangular patch resonator (RPR) for melanoma diagnosis. Operating within the [1]–[10] GHz frequency range, the RPR sensor measures dielectric contrasts between healthy and malignant tissues. At a fundamental frequency of approximately 6 GHz, it enables accurate differentiation among the four progressive stages of melanoma without the need of biopsy. Electromagnetic simulations performed using Ansys-HFSS validated this approach, and the extracted dielectric parameters showed an average relative error of only 8% compared with reference values.
Institution: University Frères Mentouri Constantine 1
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7202301508