Automated AI-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.