## 6 Conclusions  
In this study, we propose SCENet based on UNet, which integrates an SCER block and a CSAM block. The SCENet module takes advantage of stacking three small kernel convolutions to form an effective receptive field, which greatly improves feature extraction ability. The CSAM module achieved a good segmentation performance by combining spatial and channel attention mechanisms for guiding the selection of deep semantic information. The ASPP module is introduced into the bottleneck layer to obtain richer semantic features. In addition to comparing our results with the classic 3D UNet, we also compared our results with networks based on UNet or Transformers. Our results yielded WT, TC, ET, and average dice of 91.67%, 87.70%, 83.35%, and 87.57%, respectively. We also conducted ablation experiments on the SCER module, CSAM module, and ASPP, which demonstrated the effectiveness of our modules. By comparing a \(7\times 7\times 7\) convolution with three stacking \(3\times 3\times 3\) convolutions in the SCER block, it can be seen that although their receptive field values are similar, the stacking of three \(3\times 3\times 3\) convolutions yielded better results in this network. The combination experiment of ASPP and CSAM shows that multi-scale receptive fields used into the bottleneck layer are beneficial, but the improvement effect is not significant without effective feature selection in the decoder. By comparing the performance of the SCER block in SCENet with the shuffle block in ShuffleNet V2 in this architecture, the results show that the proposed module in this network is superior to the shuffle block. Furthermore, quantitative and qualitative experiments demonstrated the accuracy of SCENet in brain tumor segmentation tasks. Our architecture and the proposed modules can provide effective ideas for subsequent research. We believe that  
Figure 10: The results of comparative experiment SCER module with shuffle block.  
the encouraging results obtained with SCENet will inspire further research into brain tumor segmentation.  
Conceptualization, N.C. and B.G.; methodology, B.G.; software, P.Y.; data curation, R.Z.; writing--original draft, B.G.; writing--review and editing, N.C. and B.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jiangsu Provincial Key Research and Development Program (BE2020714). The APC was funded by [PERSON].  
Not applicable.  
Datasets released to the public were analyzed in this study. The BraTS2021 dataset can be found through the following link: [[https://www.med.upenn.edu/cbica/brats2021/#Data2](https://www.med.upenn.edu/cbica/brats2021/#Data2)]([https://www.med.upenn.edu/cbica/brats2021/#Data2](https://www.med.upenn.edu/cbica/brats2021/#Data2)) (accessed on 15 October 2024).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
AG  
Attention Gate  
AP  
Average Pooling  
ASPP  
Atrous Spatial Pyramid Pooling  
AVG  
Average Dice Value  
CNN  
Convolutional Neural Network  
CSAM  
Channel Spatial Attention Module  
Dice  
Dice Similarity Coefficient  
ET  
Enhancing Tumor  
EVO Norm  
Evolving Normalization--Activation Layers  
Expt  
Experiment  
FCN  
Fully Convolutional Networks  
FLAIR  
Fluid Attenuated Inversion Recovery  
GAP  
Global Average Pooling  
HD  
Hausdorff Distance  
LN  
Layer Normalization  
MP  
Max Pooling  
ROI  
Region Of Interest  
SCENet  
Small Kernel Convolution With Effective Receptive Field Network  
SCER  
Small Kernel Convolution With Effective Receptive Field Shuffle Module  
SGE  
Spatial Group-Wise Enhance  
T1  
T1-Weighted  
T1 ce  
T1-Enhanced Contrast  
T2  
T2-Weighted  
TC  
Tumor Core  
VIT  
Vision Transformer  
VTUnet  
Volumetric Transformer  
WHO  
World Health Organization  
WT  
Whole Tumor