Design of an Octagon-Shaped THz Photonic Crystal Fiber Biosensor for Coordinated Diabetes Detection Using Dilated Causal Convolution with Botox Multi-Head Self-Attention Network

Diabetes mellitus is a pervasive global health challenge, necessitating early and accurate diagnostic methods. Current techniques often lack the sensitivity for early-stage detection and fail to capture complex biomarker interactions. This paper proposes a novel biosensing platform integrating an octagon-shaped terahertz (THz) photonic crystal fiber (PCF) biosensor with a custom deep learning model for high-sensitivity diabetes detection. The innovative sensor geometry enhances light-matter interaction, significantly reducing signal loss. The extracted THz spectral data is processed by a Dilated Causal Convolution with Botox Multi-Head Self-Attention Network (Dil-2CBM-SAN), which optimizes feature extraction and classification. Our synergistic approach demonstrates exceptional performance, achieving a peak accuracy of 99.98% and a wavelength sensitivity of 48,000 RIU for biomarker concentrations as low as 0.1 nM, while maintaining minimal confinement loss. This work presents a groundbreaking and robust framework for early, precise diabetes diagnosis.