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

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.

Keywords

• meniscal tear
• multi kernel learning
• multi-view learning
• magnetic resonance imaging
• deep learning