[1]刘辉,朱正为,张徐,等.一种结合多尺度特征融合和混合域注意力机制的眼底疾病诊断方法[J].中国医学物理学杂志,2023,40(12):1477-1485.[doi:DOI:10.3969/j.issn.1005-202X.2023.12.005]
　LIU Hui,ZHU Zhengwei,ZHANG Xu,et al.A diagnostic method incorporating multi-scale feature fusion and hybrid domain attention mechanism for fundus diseases[J].Chinese Journal of Medical Physics,2023,40(12):1477-1485.[doi:DOI:10.3969/j.issn.1005-202X.2023.12.005]
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一种结合多尺度特征融合和混合域注意力机制的眼底疾病诊断方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第12期
页码:
1477-1485
栏目:
医学影像物理
出版日期:
2023-12-27

文章信息/Info

Title:
A diagnostic method incorporating multi-scale feature fusion and hybrid domain attention mechanism for fundus diseases
文章编号:
1005-202X（2023）12-1477-09
作者:
刘辉1; 朱正为1; 2; 张徐1; 仲慧1
1.西南科技大学信息工程学院， 四川 绵阳 621010； 2.西南科技大学特殊环境机器人技术四川省重点实验室， 四川 绵阳 621010
Author(s):
LIU Hui1;  ZHU Zhengwei1; 2;  ZHANG Xu1;  ZHONG Hui1
1. College of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China 2. Robot?echnology?sed?or?pecial?nvironment?ey?aboratory?f?ichuan?rovince,?ianyang 621010,?hina
关键词:
眼底疾病; 卷积神经网络; 多尺度特征融合; 混合域注意力机制
Keywords:
Keywords: fundus disease convolutional neural network multi-scale feature fusion hybrid domain attention mechanism
分类号:
R318；TP391.4
DOI:
DOI:10.3969/j.issn.1005-202X.2023.12.005
文献标志码:
A
摘要:
针对眼底疾病图像细微特征多、样本数量较少、诊断难度较大的问题，结合深度学习和医学影像技术，提出一种融合多尺度特征融合和混合域注意力机制的眼底疾病诊断网络模型和方法。该方法以Resnet50作为基线网络，通过对其进行改进和优化，利用并行多分支结构提取不同感受野下的眼底疾病特征，有效提高模型的特征提取能力和计算效率，采用混合域注意力机制选择对当前任务目标更关键的信息，有效提高模型的分类性能。最后利用ODIR数据集对该方法进行实验，实验结果表明，提出的方法对正常眼和不同眼底疾病的诊断准确率达到93.2%，相较于基线网络提高5.2%，诊断效果良好。
Abstract:
Abstract: In view of numerous subtle features in fundus disease images, small sample sizes, and difficulties in diagnosis, both deep learning and medical imaging technologies are used to develop a fundus disease diagnosis model that integrates multi-scale features and hybrid domain attention mechanism. Resnet50 network is taken as the baseline network, and it is modified in the study. The method uses parallel multi-branch architecture to extract the features of fundus diseases under different receptive fields for effectively improving the feature extraction ability and computational efficiency, and adopts hybrid domain attention mechanism to select information that is more critical to the current task for effectively enhancing the classification performance. The test on ODIR dataset shows that the proposed method has a diagnostic accuracy of 93.2% for different fundus diseases, which is 5.2% higher than the baseline network, demonstrating a good diagnostic performance.

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备注/Memo

备注/Memo:
【收稿日期】2023-07-11 【基金项目】国家自然科学基金（62071399）；特殊环境机器人四川省重点实验室项目（13zxtk08）；西南科技大学博士基金（17zx7159） 【作者简介】刘辉，硕士，研究方向：深度学习及其在眼底疾病诊断中的应用，E-mail: lh990707@163.com 【通信作者】朱正为，博士，副教授，研究生导师，研究方向：图像处理、人工智能和目标识别等，E-mail: zhuzwin@163.com
更新日期/Last Update: 2023-12-27