波谱学杂志 ›› 2023, Vol. 40 ›› Issue (4): 423-434.doi: 10.11938/cjmr20233076

• 研究论文 • 上一篇    下一篇

基于CNN-SVM的多维度信息融合半月板撕裂分类方法

赖嘉雯1,汪宇玲1,*(),蔡晓宇1,周丽华2   

  1. 1.东华理工大学 信息工程学院,江西 南昌 330000
    2.东华理工大学 医务所,江西 南昌 330000
  • 收稿日期:2023-07-24 出版日期:2023-12-05 在线发表日期:2023-09-15
  • 通讯作者: * Tel: 13870690380, E-mail: wangyuling_119@vip.163.com.
  • 基金资助:
    国家自然科学基金(62066003);国家留学基金项目(CSC202208360143)

Multidimensional Information Fusion Method for Meniscal Tear Classification Based on CNN-SVM

LAI Jiawen1,WANG Yuling1,*(),CAI Xiaoyu1,ZHOU Lihua2   

  1. 1. School of Information Engineering, East China University of Technology, Nanchang 330000, China
    2. School of Clinic, East China University of Technology, Nanchang 330000, China
  • Received:2023-07-24 Published:2023-12-05 Online:2023-09-15

摘要:

针对半月板计算机辅助诊断(CAD)系统中半月板撕裂形态各异带来的分类准确率低的问题,提出一种多维度信息融合网络(Multi-Dimensional Information Fusion Network,MDIFNet)模型的半月板撕裂分类方法.首先,使用由四个子网络所构成的卷积神经网络(Convolutional Neural Network,CNN)架构以获取不同视角、不同维度的半月板特征信息;同时,提出了多尺度注意力机制,丰富细粒度特征;最后,构建了基于支持向量机(Support Vector Machines,SVM)的多核模型作为最终的分类器.在MRNet数据集上的实验结果表明,本文提出方法的分类准确率达0.782,较现有先进的基于深度学习的半月板撕裂分类方法有一定提升.

关键词: 半月板撕裂, 多核学习, 多视图学习, 磁共振成像, 深度学习

Abstract:

Aiming to address the problem of low classification accuracy caused by the different shapes of meniscus tears in the computer-aided diagnosis (CAD) system for meniscus, a multidimensional information fusion network (MDIFNet) model for menissus tear classification was proposed. Firstly, a convolutional neural network (CNN) architecture consisting of four sub-networks was used to obtain meniscus feature information from different perspectives and dimensions. Simultaneously, multi-scale attention mechanism was proposed to enrich fine-grained features. Finally, a multi kernel model based on support vector machines (SVM) was constructed as the final classifier. The experimental results on the MRNet dataset show that the proposed method has a meniscal tear classification accuracy of 0.782, which has promotion compared to the existing state-of-the-art meniscus tear classification methods based on deep learning.

Key words: meniscal tear, multi kernel learning, multi-view learning, magnetic resonance imaging, deep learning

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