Single-cell classification of foodborne pathogens using hyperspectral microscope imaging coupled with deep learning frameworks
2020
Kang, Rui
A high-throughput hyperspectral microscope imaging (HMI) technology with hybrid deep learning (DL) frameworks defined as "Fusion-Net" is proposed for rapid identification of foodborne bacteria at a single-cell level. HMI technology is useful for characterization of bacterial cells, providing spatial, spectral and combined spatial-spectral profiles with high resolution, yet direct analysis of these high-dimensional HMI data is challenging. In this study, HMI data were decomposed into three features including morphology, intensity distribution, and spectral profiles of Campylobacter, E. coli, Listeria, Staphylococcus, and Salmonella. Multiple advanced DL frameworks such as long-short term memory (LSTM) network, deep residual network (ResNet), and one-dimensional convolutional neural network (1D-CNN) were employed for model development, achieving classification accuracies of 92.2%, 93.8%, and 96.2%, respectively. In addition, taking advantage of fusion strategy, individual DL framework was stacked to form "Fusion-Net" that processed aforementioned three features simultaneously, resulted in an improved classification accuracy of 98.4%. Our study demonstrates the ability of DL frameworks to assist HMI technology for single-cell classification as a diagnostic tool for rapid detection of foodborne bacteria.
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