ArticleRSC advances2022
RamanNet: a lightweight convolutional neural network for bacterial identification based on Raman spectra.
Article in RSC advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Bacterial species differentiation via real-time detection of microbial volatile organic compounds using a wavelength multiplexed photoionization detector and AI image-based analysis.Scientific reports · 2026Article
- Portable Raman spectroscopy combined with machine learning for rapid and label-free recognition of five pathogenic bacteria.Frontiers in microbiology · 2026Article
- Raman signatures ofFrontiers in microbiology · 2026Article
- Enhancing Open-World Bacterial Raman Spectra Identification by Feature Regularization for Improved Resilience against Unknown Classes.Chemical & biomedical imaging · 2024Article
- Dense Convolutional Neural Network for Identification of Raman Spectra.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
Funding
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Abstract
Raman spectroscopy combined convolutional neural network (CNN) enables rapid and accurate identification of the species of bacteria. However, the existing CNN requires a complex hyperparameters model design. Herein, we propose a new simple network architecture with less hyperparameter design and low computation cost, RamanNet, for rapid and accurate identifying of bacteria at the species level based on its Raman spectra. We verified that compared with the previous CNN methods, the RamanNet reached comparable results on the Bacteria-ID Raman spectral dataset and PKU-bacterial Raman spectral datasets, but using only about 1/45 and 1/297 network parameters, respectively. RamanNet achieved an average isolate-level accuracy of 84.7 ± 0.3%, antibiotic treatment identification accuracy of 97.1 ± 0.3%, and distinguished accuracy of 81.6 ± 0.9% for methicillin-resistant and -susceptible
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Registered trials
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