Machine learning enabled 2D photonic crystal biosensor for early cancer detection

In this paper, a novel 2D Photonic Crystal (PC)-based cancer biosensor is proposed for the detection of different types of cancer cells HeLa, PC12, MDA, MCF, and Jurkat. The sensor is designed using Silicon-on-insulator (SOI) substrate in a triangular lattice with holes in the slab. The proposed design is optimized to provide a high-quality factor of 15,000, high sensitivity and a low detection limit that are highly effective in cancer detection. Proposed biosensor uses a series of resonant cavities that slice the resonant wavelength to a high peak resonant wavelength with a spectral linewidth of 0.1 nm. The integration of 2D PC biosensors with machine learning techniques for early and accurate cancer detection is optimized for the data set. The performance analysis of Multiple Linear Regression (MLR) and Support Vector Machine (SVM) is studied by repeating training, testing, and optimization of target values (Resonant Wavelength) with dependent and independent features of a 2D PC biosensor system. The SVM model provides an R squared value of 0.99 for the biosensor, and the MLR model gave an R squared value of 0.88. The SVM model provides excellent accuracy in predicting the target values with all the trained input features of a 2D PC biosensing system.