计算机与现代化Issue(4)：103-110,8.DOI:10.3969/j.issn.1006-2475.2025.04.016
基于HA-Net模型的职业性尘肺病筛查
Occupational Pneumoconiosis Screening Based on HA-Net Model
摘要
Abstract
By combining deep learning methods and attention mechanisms,this study aims to improve the accuracy and effi-ciency of screening for occupational pneumoconiosis based on digital radiography.An improved deep learning model,hybrid at-tention 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 di-rections,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 pro-posed 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.关键词
尘肺病/注意力机制/深度学习/职业病筛查/数字化X射线摄影Key words
pneumoconiosis/attention mechanism/deep learning/occupational disease screening/digital radiography分类
计算机与自动化引用本文复制引用
王家乐,宋文爱,富丽贞..基于HA-Net模型的职业性尘肺病筛查[J].计算机与现代化,2025,(4):103-110,8.基金项目
国家青年基金资助项目(61602467) （61602467）
中北大学研究生科技立项(20231960) （20231960）
