Convolutional automatic identification of B-lines and interstitial syndrome in lung ultrasound images using pre-trained neural networks with feature fusion

Our study demonstrates the feasibility of using a pre-trained CNN as a diagnostic tool for IS screening on LUS frames, integrating targeted data filtering, feature extraction, and fusion techniques. The data-filtering technique refines the training dataset by excluding LUS frames that lack IS-related features (e.g., absence of B-lines). Feature fusion combines features learnt from different models or "fused" to enhance overall predictive performance. This study confirms the practicality of using pre-trained CNN models with feature extraction and fusion techniques for screening IS using LUS frames. This represents a noteworthy advancement in improving the efficiency of diagnosis. In the next steps, validation on larger datasets will assess the applicability and robustness of these CNN models in more complex clinical settings.