Laser speckle contrast imaging(LSCI) has been developed to measure blood perfusion non-invasively for a long time. However, there are some limitations to analyzing the random speckle phenomenon and relying on the statistical description in bio-application. This study aimed to verify the three-dimensional convolution neural network(3D-CNN) model for analyzing laser speckle images and predicting the perfusion velocity. The dataset for training deep learning was processed in the form of 3D-image and the image was from a real-time LSCI system. The model can potentially measure static and dynamic speckle information and predict perfusion velocity under the static tissue.
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