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 although the feature-based methods perform better than CNN in terms of accuracy and are computationally cheaper, the performance of CNNis already very good and may be probably improved after carrying out an expanded search for the best performing CNN model on an HPC platform .
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 .
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perform better than CNN in terms of accuracy and are computationally cheaper, the performance of CNNis already very good and may be probably improved after carrying out an expanded search for the best performing CNN model on an HPC platform
, 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