Abstract: Pneumoconiosis is a chronic progressive interstitial lung disease. Accurate staging plays an important role in diagnosis, treatment planning, and prognosis evaluation. To solve the problem that single-branch deep learning models fail to capture both global and local features, this paper proposes a two-stage dual-branch feature fusion model for intelligent staging. In the first stage, the method preprocesses chest X-ray images by using a DualAttention-Net++ segmentation network. The network applies channel–spatial attention to remove cardiac and mediastinal interference. Biorthogonal wavelet reconstruction and spatial texture fusion are used to enhance small-sample class data. In the second stage, a Dual-Branch Feature Fusion Network (DBFF-Net) is designed. The main branch based on EfficientNetV2 extracts global morphological features, while the auxiliary branch based on InceptionV3 captures multi-scale local lesion features. An adaptive feature fusion module combines complementary features from both branches. Lung regions are divided according to the GBZ70-2015 standard, and the KD-Tree algorithm locates key points to achieve accurate lung field partitioning. The method is tested on 3006 multi-center chest X-ray images, including normal and stage I–III pneumoconiosis. The model achieves 86.2% accuracy, 89% precision, 88.5% recall, 98.5% specificity, 86.1% F1-score, and 92.4% AUC. The results show that the method improves the accuracy and robustness of pneumoconiosis staging and provides a practical approach for intelligent clinical diagnosis.

摘要： 尘肺病是一种慢性进展性间质性肺病，其精准分期对临床诊断、治疗策略制定及患者预后评估具有重要意义。针对现有单分支深度学习模型难以同时提取肺部全局与局部特征、导致跨区域病灶信息丢失的问题，构建了一种两阶段的双分支特征融合分期模型。第一阶段进行胸部X线影像数据预处理，利用DualAttention-Net++分割网络，通过通道与空间双注意力机制去除心脏及纵膈干扰，并结合Biorthogonal小波频域重构与空间纹理融合策略实现小样本类别数据增强。第二阶段设计双分支特征融合网络（Dual-Branch Feature Fusion Network，DBFF-Net），主分支采用EfficientNetV2提取全局形态特征，辅分支基于InceptionV3网络多尺度提取局部病灶特征，二者通过自适应融合模块实现跨模态特征互补。局部区域划分基于GBZ70-2015肺野分区规范，采用KD-Tree算法定位关键点以实现精确区域分割。基于来自多家医院的3006张胸片数据（含正常及尘肺I–III期）进行验证，模型在分期任务中的准确率为86.2%，精确率为89%，召回率为88.5%，特异性为98.5%，F1分数为86.1%，AUC值为92.4%。结果表明，该方法能够有效提高尘肺病影像分期的准确性与鲁棒性，为临床自动化诊断提供可行的技术途径。