# Classification of diffusion modes in single-particle tracking data: Feature-based versus deep-learning approach

## TL;DR

This paper compares deep learning and classical machine learning methods for classifying diffusion modes in single-particle tracking data, finding CNNs generally outperform feature-based methods but with higher computational costs.

## Contribution

It introduces a CNN-based approach for diffusion mode classification and systematically compares it to traditional feature-based methods using simulated data.

## Key findings

- CNN slightly outperforms classical methods in accuracy
- Classical methods are faster and sometimes better in borderline cases
- Deep learning requires more computational resources

## Abstract

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.