Abstract
Manual lung ultrasound (LUS) scoring is influenced by clinicians' subjective interpretation, leading to potential inconsistencies and misdiagnoses due to varying levels of experience. To improve monitoring of pulmonary ventilation and support early diagnosis, we propose an automated LUS scoring network based on an 8-point scale, named the detailed-global fusion residual network (DGF-ResNet). This network combines local and global features using the hybrid feature fusion Block, which includes the detail feature extraction (DFE) and global feature extraction (GFE) Modules. The DFE module employs a local channel and spatial attention mechanism to capture fine details, while the GFE Module utilizes a three-order recursive gated convolution and a global channel and spatial attention mechanism to extract global features. Experimental results on the FCSPF-13324 dataset from the Second Affiliated Hospital of Zhejiang University show that DGF-ResNet outperforms VGG16, ResNet50, and Vision Transformer in accuracy, precision, recall, and F1-score. Specifically, DGF-ResNet improves over Vision Transformer by 7.05, 4.52, and 5.89 percentage points, over VGG16 by 3.06, 4.37, and 3.8 points, and over ResNet50 by 2.05, 4.26, and 3.34 points, respectively.