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Deep Learning Approach for Enhanced Detection of Surface Plasmon Scattering.

08:00 EDT 9th July 2019 | BioPortfolio

Summary of "Deep Learning Approach for Enhanced Detection of Surface Plasmon Scattering."

A deep learning approach has been taken to improve detection characteristics of surface plasmon microscopy (SPM) of light scattering. Deep learning based on convolutional neural network algorithm was used to estimate the effect of scattering parameters mainly the number and the position 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 six times. 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.

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This article was published in the following journal.

Name: Analytical chemistry
ISSN: 1520-6882
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