DiffRaman: A conditional latent denoising diffusion probabilistic model for enhancing bacterial identification via...

Raman spectroscopy has attracted significant attention in various biochemical detection fields, especially in the rapid identification of pathogenic bacteria. The integration of this technology with deep learning to facilitate automated bacterial Raman spectroscopy diagnosis has emerged as a key focus in recent research. However, the diagnostic performance of existing deep learning methods largely depends on a sufficient dataset, and in scenarios where there is a limited availability of Raman spectroscopy data, it is inadequate to fully optimize the numerous parameters of deep neural networks. To address these challenges, this paper proposes a data generation method utilizing deep generative models to expand the data volume and enhance the recognition accuracy of bacterial Raman spectra. Specifically, we introduce DiffRaman, a conditional latent denoising diffusion probability model for Raman spectra generation. Our approach begins with applying a two-dimensional figure transformation to the Raman spectral data. Following this, we utilize the encoder of a Vector Quantized Variational Autoencoder (VQ-VAE) to compress the Raman image into a lower-dimensional latent space. We then construct a Conditional Denoising Diffusion Probabilistic Model (DDPM) for representation learning and data augmentation. Ultimately, the decoder of the VQ-VAE is employed to reconstruct the spectrum from its low-dimensional latent representation. Experimental results show that DiffRaman-generated synthetic bacterial Raman spectra can effectively mimic real spectra, improving diagnostic model performance, particularly in data-limited settings. Compared to existing models, DiffRaman enhances generation quality and computational efficiency. Our DiffRaman approach offers a well-suited solution for automated bacteria Raman spectroscopy diagnosis in data-scarce scenarios, offering new insights into alleviating the labor of spectroscopic measurements and enhancing rare bacteria identification.