Machine learning assisted malaria detection using photonic crystal fibre optical sensors.

This study presents a photonic crystal fibre based optical sensor with a simple and practical architecture for malaria detection using refractive index variations in red blood cells. The proposed sensor consists of a hollow central core surrounded by five concentric layers of heptagonal cladding, enabling efficient sample infiltration and enhanced light matter interaction. This configuration provides high sensitivity to subtle refractive index changes while maintaining structural simplicity suitable for real world diagnostic deployment. Refractive index variations corresponding to different Plasmodium developmental stages are converted into distinct wavelength shifts, allowing reliable discrimination between ring trophozoite and schizont stages. The sensor operates over a refractive index range of 1.373 to 1.402, closely matching the optical properties of malaria infected red blood cells. Numerical results demonstrate high relative sensitivity of 97.45% for healthy cells, 96.89% for the ring stage, 96.22% for the trophozoite stage, and 95.45% for the schizont stage. Optical confinement losses remain extremely low, on the order of 10–8 dB per metre at an operating frequency of 2.2 THz. These results highlight the potential of photonic crystal fibre sensors as a cost effective and high-performance platform for early malaria detection and broader biomedical sensing applications.