Rapid identification of bacterial pathogens by Raman spectroscopy and transformer

Bo Zhou; Ru Zhang; Anpei Ye

doi:10.1117/12.2689550

31 July 2023 Rapid identification of bacterial pathogens by Raman spectroscopy and transformer

Bo Zhou, Ru Zhang, Anpei Ye

Proceedings Volume 12747, Third International Conference on Optics and Image Processing (ICOIP 2023); 127470O (2023) https://doi.org/10.1117/12.2689550
Event: Third International Conference on Optics and Image Processing (ICOIP 2023), 2023, Hangzhou, China

Abstract

Raman spectroscopy offers numerous advantages in bacterial identification, including rich molecular information, quick processing, and great sensitivity. However, accurately identifying bacterial species remains challenging due to the similarity of Raman spectra among various species. This paper introduces a method that combines Transformer networks and Raman spectra for the swift and precise identification of pathogenic bacteria. Our lightweight transformer model, called RamanFormer, outperforms conventional convolutional neural network (CNN) models in identification accuracy and model complexity on the Bacteria-ID dataset and Custom-built dataset. RamanFormer has only about 1/35 and 1/184 of the network parameters compared with CNNs. On the Bacteria-ID dataset, RamanFormer reached a state-of-the-art (SOTA) isolate-level accuracy of 87.03%. We also evaluated the model using clinical bacterial isolates and discovered that it had a SOTA of 99.98% identification accuracy in the 8-antibiotic empiric group task using just ten bacterial spectra per patient isolate. Additionally, RamanFormer also achieved 97.32% identification accuracy on the Custombuilt dataset. Our approach is thus capable of quickly and correctly classifying different bacterial pathogens based on the Raman spectra and could be used for additional Raman spectra identification tasks. The code for RamanFormer will be accessed at https://github.com/Bo-Zhou-gogogo/Raman-transformer.

Citation Download Citation

Bo Zhou, Ru Zhang, and Anpei Ye "Rapid identification of bacterial pathogens by Raman spectroscopy and transformer", Proc. SPIE 12747, Third International Conference on Optics and Image Processing (ICOIP 2023), 127470O (31 July 2023); https://doi.org/10.1117/12.2689550

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KEYWORDS

Raman spectroscopy

Data modeling

Feature extraction

Pathogens

Transformers

Education and training

Bacteria

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