  Single-particle trajectories measured in microscopy experiments contain
important information about dynamic processes undergoing in a range of
materials including living cells and tissues. However, extracting that
information is not a trivial task due to the stochastic nature of particles'
movement and the sampling noise. In this paper, we adopt a deep-learning method
known as a convolutional neural network (CNN) to classify modes of diffusion
from given trajectories. We compare this fully automated approach working with
raw data to classical machine learning techniques that require data
preprocessing and extraction of human-engineered features from the trajectories
to feed classifiers like random forest or gradient boosting. All methods are
tested using simulated trajectories for which the underlying physical model is
known. From the results it follows that CNN is usually slightly better than the
feature-based methods, but at the costs of much longer processing times.
Moreover, there are still some borderline cases, in which the classical methods
perform better than CNN.
