A Novel Improved Brain Tumor Segmentation Method Using Deep Learning Network

Abstract

Aiming at the problems of uneven brain tumor data classification and insufficient feature extraction, an improved brain tumor segmentation (BTS) method using deep learning network is proposed in this study. Here, we use U-net as to be the main network architecture, combined with the advantages of the residual network Resnet, which uses skip connections in each layer of encoding and decoding to form a residual module to avoid the disappearance of the gradient. Data enhancement is applied in data processing. To further improve the processing performance, we add a learning mechanism to the network and incorporates the compression and excitation module scSE on both space and channel to extract more useful features. This article is verified on the BraTS 2018 public brain data set. On the 66 officially provided verification sets, the network after adding the scSE module has obtained better segmentation results for the entire tumor, tumor core and enhanced tumor.