Deep Learning Based Acquisitional Denoising for Raman Spectroscopy using CNN and transformer

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.