RamanNet: A generalized neural network architecture for Raman Spectrum Analysis
This addresses a gap in Raman spectroscopy applications for fields like medical diagnostics and forensics, though it appears incremental as it builds on existing methods.
The authors tackled the lack of generalized machine learning methods for Raman spectrum analysis by proposing RamanNet, a novel neural network architecture that combines strengths from sequential and traditional models, achieving superior performance on 4 public datasets over state-of-the-art methods.
Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kind of materials. This sort of fingerprinting molecules has thus led to widespread application of Raman spectrum in various fields like medical dignostics, forensics, mineralogy, bacteriology and virology etc. Despite the recent rise in Raman spectra data volume, there has not been any significant effort in developing generalized machine learning methods for Raman spectra analysis. We examine, experiment and evaluate existing methods and conjecture that neither current sequential models nor traditional machine learning models are satisfactorily sufficient to analyze Raman spectra. Both has their perks and pitfalls, therefore we attempt to mix the best of both worlds and propose a novel network architecture RamanNet. RamanNet is immune to invariance property in CNN and at the same time better than traditional machine learning models for the inclusion of sparse connectivity. Our experiments on 4 public datasets demonstrate superior performance over the much complex state-of-the-art methods and thus RamanNet has the potential to become the defacto standard in Raman spectra data analysis