A Dual Attention Mechanism for Detection and Assessment Pulmonary Fibrosis in Chest X-Ray Images

Pulmonary fibrosis is a progressive lung disease characterized by interstitial tissue fibrosis, with idiopathic pulmonary fibrosis being the most common subtype. The disease leads to persistent scarring and fibrotic remodeling of lung tissue following injury, and under current medical conditions, it remains incurable and irreversible. Chest X-ray imaging is widely used in clinical diagnosis; however, traditional interpretation methods face challenges in accurately detecting fibrotic lesions and quantifying disease severity. To address these limitations, we propose the Attention Feature Transformation Network (AFTNet) for automated identification and precise quantification of pulmonary fibrosis regions. AFTNet integrates two key modules—Spatial-Based Multi-Head Self-Attention (SB-MSA) and Channel-Based Multi-Head Self-Attention (CB-MSA)—which enhance the model’s ability to capture subtle features in chest X-ray images. In addition, a specially designed skip connection mechanism preserves multi-level details and improves boundary delineation accuracy. Experimental results demonstrate that AFTNet outperforms comparative models, achieving superior performance with IoU (80.34%), Dice (81.76%), and Hausdorff Distance (21.54%). Furthermore, by incorporating data augmentation strategies, the model shows robust adaptability to chest X-ray images under different clinical conditions, enabling accurate boundary detection of fibrotic regions and quantifying fibrosis extent. The proposed AFTNet provides an efficient, accurate, and clinically valuable solution for the quantitative analysis of pulmonary fibrosis, contributing to improved diagnostic efficiency and treatment monitoring in pulmonary diseases.