Benchmarking deep learning models for Raman spectroscopy across open-source datasets

2026/01/01 by Adithya Sineesh, Akshita Ramya Kamsali
Biochemistry, Genetics and Molecular Biology · Chemistry · Engineering · #Spectroscopy Techniques in Biomedical and Chemical Research #Spectroscopy and Chemometric Analyses #Remote-Sensing Image Classification

paper · pdf · doi:10.1039/d6dd00044d

Abstract

We benchmark five published Raman-specific deep learning classifiers across three open-source Raman datasets, providing one of the first reproducible comparisons of three or more Raman-specific deep learning models for supervised classification.

Citations

• A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders
• Deep Learning Assisted Raman Spectroscopy for Rapid Identification of 2D Materials
• RamanNet: A generalized neural network architecture for Raman Spectrum Analysis
• Refinement of spectra using a deep neural network: Fully automated removal of noise and background
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Denoising Diffusion Probabilistic Models
• Attention Is All You Need
• Densely Connected Convolutional Networks
• Deep Residual Learning for Image Recognition
• Rethinking the Inception Architecture for Computer Vision
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Breaking with trends in pre-processing?
• ImageNet classification with deep convolutional neural networks
• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
• Gradient-based learning applied to document recognition

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