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Photonic crystal biosensors offer exceptional sensitivity for biomedical diagnostics. This study proposes a GaAs rod-based photonic crystal biosensor for early breast cancer detection, operating at 1550 nm with a hexagonal lattice structure to enhance light confinement. Biomarker interactions induce a resonant wavelength shift, enabling high-precision refractive index sensing. Machine learning optimization fine-tunes structural parameters, improving spectral resolution and diagnostic accuracy. Simulations confirm the sensor’s potential for real-time, label-free, and non-invasive screening, providing a cost-effective solution for early cancer detection.

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Machine Learning-Optimized Photonic Crystal-Based Biosensor for Early Detection of Breast Cancer

  • 会议论文

  • 发表日期：

    2026年

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  • V. Anusooya,
  • S. Nithyasri,
  • Mohit Mayank,
  • R. Pavya

摘要

Photonic crystal biosensors offer exceptional sensitivity for biomedical diagnostics. This study proposes a GaAs rod-based photonic crystal biosensor for early breast cancer detection, operating at 1550 nm with a hexagonal lattice structure to enhance light confinement. Biomarker interactions induce a resonant wavelength shift, enabling high-precision refractive index sensing. Machine learning optimization fine-tunes structural parameters, improving spectral resolution and diagnostic accuracy. Simulations confirm the sensor’s potential for real-time, label-free, and non-invasive screening, providing a cost-effective solution for early cancer detection.