基于CNN-SVM的多维度信息融合半月板撕裂分类方法

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

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.

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.