机电工程技术2026，Vol.55Issue(17)：19-25,7.DOI:10.3969/j.issn.1009-9492.2026.17.004
融合双注意力机制的骨质疏松分类算法
A Dual-attention Mechanism Fusion Algorithm for Osteoporosis Classification
摘要
Abstract
An osteoporosis classification algorithm with a fused dual-attention mechanism,named R-ENet is proposed.The method builds upon the EfficientNetV2 architecture and optimizes the original EfficientNetV2-small network using a grouping strategy,which ensures lightweight computation while enhancing the model's ability to recognize fine-grained features.Additionally,the original classification head is improved with a novel structure to further boost feature discrimination and classification accuracy.The model classifies knee X-ray images into three categories:osteoporosis,osteopenia,and normal.A publicly available knee X-ray dataset is used to train,validate,and test the proposed model.Experimental results demonstrate that the proposed model achieves the highest classification accuracy on the dataset,reaching 95.0%,which represents a 3.0%improvement over the best-performing baseline network.This work enhances the diagnostic accuracy of deep learning in osteoporosis detection and provides a valuable reference for future image-based diagnostic processes.关键词
X射线图像/骨质疏松/双注意力机制/分类器/图像分类/卷积神经网络Key words
X-ray imaging/osteoporosis/dual-attention mechanism/classifier/image classification/convolutional neural network分类
信息技术与安全科学引用本文复制引用
王庆顺,王沫楠..融合双注意力机制的骨质疏松分类算法[J].机电工程技术,2026,55(17):19-25,7.基金项目
国家自然科学基金(61972117) （61972117）
黑龙江省双一流学科协同创新成果项目(LJGXCG2025-P03) （LJGXCG2025-P03）
