Deep Convolutional Neural Networks as a Unified Solution for Raman
Spectroscopy-Based Classification in Biomedical Applications
Abstract
Machine learning has shown great potential for classifying diverse
samples in biomedical applications based on their Raman spectra.
However, the acquired spectra typically require several preprocessing
steps before standard machine learning algorithms can accurately and
reliably classify them. To simplify this workflow and enable future
growth of this technology, we present a unified solution for classifying
biological Raman spectra without any need of prepossessing, including
denoising and baseline establishment. This method is developed based on
a custom version of a convolutional neural network (CNN) elicited from
ResNet architecture, combined with our proposed data augmentation
technique. The superiority of this method compared to conventional
classification techniques is shown by applying it to Raman spectra of
different grades of bladder cancer tissue and surface enhanced Raman
spectroscopy (SERS) spectra of various strains of E. Coli extracellular
vesicles (EVs). These results show that our method is far more robust
compared to its conventional counterparts when dealing with the various
kinds of spectral baselines produced by different Raman spectrometers.