Deep Neural Deconvolution of Plasmonic Signal Interference for Ultra-Sensitive Lung Cancer Biomarker Detection

Early and sensitive identification of the lung cancer biomarkers is essential towards the process of enhancing the patient outcomes. Traditional plasmonic biosensors SP, LSPR, and SERS, provide sensitive detection of other biosensors, but spectral overlap, interference, and poor reproducibility in multi-biomarkers. Here, the plasmonic biosensing framework is augmented with AI and uses a convolutional neural network (CNN) with the sensors to facilitate real-time, automated deconvolution of overlapping resonance signals. CNN model was minimized to use convoluted layers of 4-6, ReLU activation, and 3x3 or 5x5 kernels, where the return accuracy in the prediction was up to 97.2 percent and a mean squared error was 0.0015 with single- and multi-biomarker spectra. It achieved 3-4x better signal-to-interference ratio, better resonance shift errors (0.012 - 0.015 nm) and a processing latency of less than 1s/sample compared to other integrations with SPR and LSPR sensors. Synthetic and clinical sample confirmation showed that the classification accuracy is over 95 percent, mean absolute error is less than 0.018 nm and reproducible variance smaller than 0.02 nm. Scalability test mains ensured that steady performance was achieved even with seven biomarkers and a variety of sensor types with generalization error of less than 0.002. These findings confirm that the proposed AI-assisted plasmonic system is a robust, sensitive, and fast platform of multi-biomarker lung cancer detection, which can be applied to clinical and pointof- care uses.