Automated <scp>AI</scp> ‐Based Lung Disease Classification Using Point‐of‐Care Ultrasound

ABSTRACT Timely and accurate diagnosis of lung diseases is critical for reducing related morbidity and mortality. Lung ultrasound (LUS) has emerged as a useful point‐of‐care tool for evaluating various lung conditions. However, interpreting LUS images remains challenging due to operator‐dependent variability, low image quality, and limited availability of experts in many regions. In this study, we present a lightweight and efficient deep learning model, ParSE‐CNN, alongside fine‐tuned versions of VGG‐16, InceptionV3, Xception, and Vision Transformer architectures, to classify LUS images into three categories: COVID‐19, other lung pathology, and healthy lung. Models were trained using data from public sources and Ugandan healthcare facilities, and evaluated on a held‐out Ugandan dataset. Fine‐tuned VGG‐16 achieved the highest classification performance with 98% accuracy, 97% precision, 98% recall, and a 97% F1‐score. ParSE‐CNN yielded a competitive accuracy of 95%, precision of 94%, recall of 95%, and F1‐score of 97% while offering a 58.3% faster inference time (0.006 s vs. 0.014 s) and a lower parameter count (5.18 M vs. 10.30 M) than VGG‐16. To enhance input quality, we developed a preprocessing pipeline, and to improve interpretability, we employed Grad‐CAM heatmaps, which showed high alignment with radiologically relevant features. Finally, ParSE‐CNN was integrated into a mobile LUS workflow with a PC backend, enabling real‐time AI‐assisted diagnosis at the point of care in low‐resource settings.