Fast analysis of time‐domain fluorescence lifetime imaging via extreme learning machine

Zang, Zhenya and Xiao, Dong and Wang, Quan and LI, Zinuo and Xie, Wujun and Chen, Yu and Li, David Day Uei (2022) Fast analysis of time‐domain fluorescence lifetime imaging via extreme learning machine. Sensors, 22 (10). 3758. ISSN 1424-8220 (https://doi.org/10.3390/s22103758)

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Abstract

We present a fast and accurate analytical method for fluorescence lifetime imaging microscopy (FLIM), using the extreme learning machine (ELM). We used extensive metrics to evaluate ELM and existing algorithms. First, we compared these algorithms using synthetic datasets. The results indicate that ELM can obtain higher fidelity, even in low-photon conditions. Afterwards, we used ELM to retrieve lifetime components from human prostate cancer cells loaded with gold nanosensors, showing that ELM also outperforms the iterative fitting and non-fitting algorithms. By comparing ELM with a computational efficient neural network, ELM achieves comparable accuracy with less training and inference time. As there is no back-propagation process for ELM during the training phase, the training speed is much higher than existing neural network approaches. The proposed strategy is promising for edge computing with online training.

ORCID iDs

Zang, Zhenya, Xiao, Dong, Wang, Quan, LI, Zinuo, Xie, Wujun ORCID logoORCID: https://orcid.org/0000-0003-0483-8639, Chen, Yu and Li, David Day Uei ORCID logoORCID: https://orcid.org/0000-0002-6401-4263;
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