Classification of Current Density Vector Maps for Heart Failures Using a Transfer Convolutional Neural Network
Zhenghui Hu, Yutong Lin, Kaikai Ye, Qiang Lin
Abstract
Ischemia heart disease(IHD) is the leading cause of death in worldwide. Magnetocardiogram( MCG) as a non-invasive detection of heart, takes more important role in clinic detection. However, the MCG technique is not a common diagnosis tool in routine clinical due to lack of MCG data and trained doctors for MCG data, especially for current density vector map(CDVM). Therefore, we propose an automatic method to analyze MCG data by using deep learning method. Here, we propose a deep learning method called Residual Network(ResNet) with transfer learning to classify CDVM from category 0 to category 4, which is reconstruct from MCG data. The ResNet exhibits accuracy of 90.02%. This paper suggests a high potential of applying ResNet for CDVMs.