Abstract
This study explores the application of optical flow techniques for the automated classification of lung sliding in lung ultrasound—a key indicator for assessing lung function and diagnosing pneumothorax. Optical flow is used to capture subtle pleural motion between frames. We evaluate both traditional machine learning models (Random Forest, Gradient Boosting) and deep learning architectures (CNN, ResNet-18), using various preprocessing methods to optimize flow representation. Among deep models, the CNN achieved the highest overall accuracy (80.8%) and F1-score (73.7%), while the more complex ResNet-18 reached a high recall (94.5%) but suffered from lower precision (62.6%), indicating a tendency to over-predict the positive class. These findings highlight the trade-offs between model complexity and generalization in limited-data scenarios. The results underline the importance of model selection and tailored preprocessing in improving diagnostic reliability.