Abstract:  By combining deep learning methods and attention mechanisms， this study aims to improve the accuracy and efficiency of screening for occupational pneumoconiosis based on digital radiography. An improved deep learning model， hybrid attention network （HA-Net）， is proposed， which integrates squeeze-and-excitation block （SEB） and coordinate attention block （CAB） to enhance feature representation capabilities. SEB extracts inter-channel relationship information through global average pooling， uses fully connected layers to adjust channel weights， and multiplies the adjusted weights with the original input feature maps to strengthen important features. CAB captures spatial information through global pooling in both horizontal and vertical directions， then generates attention weights via 1×1 convolution and channel restoration， which are subsequently multiplied with the feature maps processed by SEB. Finally， these components are integrated into the ResNet50V2 model to distinguish between pneumoconiosis and non-pneumoconiosis images and accurately screen suspected cases. Experimental results show that the proposed model performs excellently in the task of screening occupational pneumoconiosis with high accuracy. It can reliably detect pneumoconiosis cases and also demonstrates high precision and sensitivity in identifying suspected cases.

Key words: pneumoconiosis, attention mechanism, deep learning, occupational disease screening, digital radiography