DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins

We present the effectiveness of <i>DeepRaman</i>, an innovative architecture inspired by the Progressive Fourier Transform and integrated with the scalogram transformation method, in classifying raw SERS Raman spectral data from biological specimens with unparalleled accuracy relative to conventional machine learning algorithms. Notably, this Convolutional Neural Network (CNN) operates autonomously, requiring no human intervention, and can be applied with substantially smaller datasets than traditional CNNs. Furthermore, it exhibits remarkable proficiency in managing challenging baseline scenarios that often lead to failures in other techniques, thereby promoting the broader clinical adoption of Raman spectroscopy.