MPEDA-Net: A lightweight brain tumor segmentation network using multi-perspective extraction and dense attention

Malignant brain tumors are highly deadly, necessitating the quickly precise segmentation of tumor regions. Previously, clinicians manually classified brain tumor regions utilizing magnetic resonance imaging (MRI). A new trend is the use of computer vision processing to assist clinicians in clinical analysis. Even though numerous recent methodologies based on CNN have been presented, there remains a lack of high performance when evaluating regions in MRI images. Furthermore, there is still a possibility for development in terms of parameter number and computational complexity. To collect feature information and improve the relevance of contextual feature extraction, a multi-perspective extraction (MPE) module is proposed. MPE consists of three different convolutional kernels and special operations. In addition, a dense attention (DA) module is used to provide each point with an appropriate level of attention while fusing features. The effectiveness of these two modules has been proved through ablation experiments. The proposed MPEDA-Net achieves dice of 82.52%, 93.07%, and 87.67% on the R-BraTS2021 (reconstructed-BraTS2021) in the ET, WT, and TC respectively. In addition, the BraTS2018 and BraTS2019 experiments illustrate that the dice of ET, WT, and TC reach 82.44%, 91.38%, 88.27%, and 83.73%, 91.87%, 88.71%, respectively. The more effective segmentation performance shows that MPEDA-Net can significantly enhance brain tumor segmentation accuracy, exceeding several existing methods. The MPEDA-Net code is already available on GitHub: https://github.com/luohaohaoluo/MPEDANet-pytorch.