基于改进Swin Transformer的膝骨关节炎X光影像自动诊断
Knee osteoarthritis based on improved Swin Transformer X-ray image automatic diagnosis
许超 1王云健 1刘洋 1卢雪梅 1丁勇1
作者信息
摘要
膝骨关节炎是老年人群体的常见疾病,具有较高的致残性.依托深度学习算法开展膝骨关节炎的自动诊断,具有重要的应用价值.为此,提出了一种基于改进Swin Transformer模型的膝骨关节炎X光影像自动诊断算法.通过两层全连接层加ReLU激活函数的结构替换颈部网络的全局平均池化层,对迁移学习进行保护;在头部网络中添加全连接层与Tanh激活函数,组合出更多非线性特征;在数据预处理和模型训练过程中,分别依托Albumentations库和添加 Mixup模块以此实现数据增强处理.实验结果表明,所提算法能够有效提升膝骨关节炎X光影像的分类精度,在Kaggle网站的公开数据集上诊断精度达到76.0%;同时,经过在其他膝骨关节炎X光影像数据集与不同领域的医学影像数据集上进行泛化实验,结果表明其具有较好的泛化能力,进一步证明所提算法的有效性.
Abstract
Knee osteoarthritis is a common disease in the elderly population,which is highly disabling.Automatic diagnosis of knee osteoarthritis based on deep learning algorithm has important application value.Therefore,an automatic diagnosis algorithm of knee osteoarthritis based on improved Swin Transformer model is proposed.The transfer learning is protected by replacing the global average pooling layer of the neck network with a two-layer fully connected layer plus ReLU activation function.Adding full connection layer and Tanh activation function to the head network to combine more nonlinear features;in the process of data preprocessing and model training,data enhancement is realized by relying on Albumentations library and adding Mixup module respectively.The experimental results show that the proposed algorithm can effectively improve the classification accuracy of X-ray images of knee osteoarthritis,and the diagnostic accuracy reaches 76.0%on the public data set of Kaggle website.At the same time,the generalization experiments on other X-ray image data sets of knee osteoarthritis and medical image data sets in different fields show that it has good generalization ability,which further proves the effectiveness of the proposed algorithm.
关键词
膝骨关节炎/Swin/Transformer/全局平均池化/数据增强Key words
knee osteoarthritis/Swin Transformer/automatic diagnosis/data augmentation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据