Design of encoded graphene-gold metasurface-based circular ring and square sensors for brain tumor detection and optimization using XGBoost algorithm

Abstract

This study introduces a circular ring and square metasurfaces-based sensor employing graphene and gold layers for enhanced brain tumor detection. The sensor architecture includes dual graphene circular metasurfaces and a gold square resonator layer on a silicon dioxide substrate. A comprehensive parametric investigation explores the influence of geometric parameters and graphene chemical potential on sensor performance. The sensor achieves a remarkable sensitivity of 769 GHzRIU−1, a high-quality factor of 6.514, a low detection limit of 0.182 RIU, and an FOM of 1.148RIU−1. Electric field intensity analysis identifies optimal transmission frequencies. By employing the XGBoost regressor, the analysis significantly reduces simulation time and resource consumption by at least 80 %. Simulation validation confirms that despite 80 % reduction in simulation time, high prediction accuracy remains achievable, with an impressive R2 score ranging from 0.9 to 1. The proposed sensor presents a promising non-invasive approach for early brain tumor diagnosis, with potential applications in biomedical imaging and other related fields.