A deep learning approach has been taken to improve detection characteristics of surface plasmon microscopy (SPM) of light scattering. Deep learning based on the convolutional neural network algorithm was used to estimate the effect of scattering parameters, mainly the number of scatterers. The improvement was assessed on a quantitative basis by applying the approach to SPM images formed by coherent interference of scatterers. It was found that deep learning significantly improves the accuracy over conventional detection: the enhancement in the accuracy was shown to be significantly higher by almost 6 times and useful for scattering by polydisperse mixtures. This suggests that deep learning can be used to find scattering objects effectively in the noisy environment. Furthermore, deep learning can be extended directly to label-free molecular detection assays and provide considerably improved detection in imaging and microscopy techniques.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation (NRF) grants funded by the Korean Government (Grants 2018R1D1A1B07042236 and 2019R1F1A1063602).
© 2019 American Chemical Society.
All Science Journal Classification (ASJC) codes
- Analytical Chemistry