Deep-learning-based acquisitional denoising for Raman spectroscopy using CNN and transformer

Marilyn Lionts; Ezekiel Haugen; Anita Mahadevan-Jansen; Yuankai Huo

doi:10.1117/12.3027465

4 October 2024 Deep-learning-based acquisitional denoising for Raman spectroscopy using CNN and transformer

Marilyn Lionts, Ezekiel Haugen, Anita Mahadevan-Jansen, Yuankai Huo

Proceedings Volume 13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; 1311807 (2024) https://doi.org/10.1117/12.3027465
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

Raman spectroscopy (RS) is a real-time, label-free, and non-invasive spectral sensing technique that can quantify the biochemical composition of biological tissues and other substances. However, Raman scattering is a weak effect and relies on long acquisition times across multiple acquisitions to produce a robust signal. Decreasing this collection time, as required in many time-sensitive in-vivo clinical applications, results in a signal with significant noise, which hinders interpretation. Various machine-learning (ML) denoising methods have been proposed for analyzing RS signals, but very few have successfully provided an accurate acquisitional denoising algorithm that works on a broad dataset across various real-life use cases. In this pilot project, we assess the feasibility of using convolutional neural networks (CNNs) and encoder-decoder transformer -based models for acquisitional spectral denoising. We utilize in vivo RS data from the human esophagus for testing our model to demonstrate its robustness on low signal-to-noise ratio spectra.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Marilyn Lionts, Ezekiel Haugen, Anita Mahadevan-Jansen, and Yuankai Huo "Deep-learning-based acquisitional denoising for Raman spectroscopy using CNN and transformer", Proc. SPIE 13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, 1311807 (4 October 2024); https://doi.org/10.1117/12.3027465

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