Efficient and optimized blood cancer detection using engineered graphene-based silicon-TiN-silicon multilayered plasmonic sensor design with behaviour prediction using machine learning.

Blood cancer can be fatal if not detected early; innovative biosensors with machine learning optimization enable timely diagnosis by identifying cancer-specific biomarkers in blood, improving survival rates through earlier intervention and targeted treatment. A graphene-based sensor, crafted with advanced materials, enhances sensitivity for rapid and early blood cancer detection, offering improved diagnostic accuracy and timely medical intervention for better patient outcomes. Machine learning optimization is used to achieve higher sensitivity. The graphene sensor achieves a maximum sensitivity of 1430 nm/RIU, enabling highly accurate and efficient blood cancer detection performance. The developed sensor demonstrates an impressive detection limit of 0.044, offering exceptional precision and sensitivity, making it highly effective for early-stage blood cancer diagnosis and clinical applications. The optimized sensor design achieves a high-quality factor of 125 and a figure of merit of 121, indicating excellent performance, sharp resonance, and enhanced precision for blood cancer detection applications. Optimization is achieved using parametric optimization. Optimization of the sensor is accomplished through detailed parametric analysis, resulting in a finely tuned design. This optimized structure significantly enhances sensitivity and detection speed, making it a highly suitable choice for early-stage and rapid blood cancer diagnosis, thereby improving the chances of timely treatment and patient survival.