基于CNN-SVM的多维度信息融合半月板撕裂分类方法
收稿日期: 2023-07-24
网络出版日期: 2023-09-15
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
Received date: 2023-07-24
Online published: 2023-09-15
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．
赖嘉雯 , 汪宇玲 , 蔡晓宇 , 周丽华 . 基于CNN-SVM的多维度信息融合半月板撕裂分类方法[J]. 波谱学杂志, 2023 , 40(4) : 423 -434 . DOI: 10.11938/cjmr20233076
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.
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