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Found 2 papers, 2 papers with code
Interpretable Classification of Bacterial Raman Spectra with Knockoff Wavelets
Deep neural networks and other sophisticated machine learning models are widely applied to biomedical signal data because they can detect complex patterns and compute accurate predictions.
Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
By amassing the largest known dataset of bacterial Raman spectra, we are able to apply state-of-the-art deep learning approaches to identify 30 of the most common bacterial pathogens from noisy Raman spectra, achieving antibiotic treatment identification accuracies of 99. 0$\pm$0. 1%.
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