  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. 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.
