# New Approach for Brain Tumor Segmentation Based on Gabor Convolution and Attention Mechanism
## 3 Results
### Analysis of Results  
In this paper, the proposed network Ga-U-Net is analyzed and compared with the two basic networks U-Net and Attention U-Net. In addition, five other models are also tested with the dataset BraTS 2021, and the two metrics of the Dice coefficient and Hausdorff distance (HD95) proposed above are used to evaluate the segmentation results obtained for three regions, including the enhanced tumor region (ET), tumor core region (TC), and whole tumor region (WT). The comparison results for the different models are shown in Tables 2 and 3. As can be seen from Table 2, the overall performance of the network Ga-U-Net proposed in this paper is higher than that of the other models. It can achieve the highest average value for the Dice coefficients. Clearly, the Dice coefficients of the proposed approach on the enhanced tumor region (ET), the tumor core region (TC), and the whole tumor region (WT) are 0.856, 0.897, and 0.910, respectively. An improvement of 0.017, 0.02, and 0.003 is thus achieved by the proposed approach on ET, TC, and WT, respectively. It is also clear from Table 2 that the improvements in the segmentation accuracy for WT are not as significant as those for ET and TC. This is probably due to the fact that the Gabor convolution in the GA module is able to capture the local structural and textural information of the image more accurately. In addition, the results in Table 2 suggest that the CBAM attention mechanism improves the weights of the features of the tumor core region, suppresses the surrounding irrelevant regions as much as possible, and thus enables the network to locate the region of interest more accurately. Therefore, the segmentation accuracy on the tumor site is improved. Figure 7: The Hausdorff distance. Table 3 shows a comparison of the Hausdorff distances achieved by all tested methods. It can be seen from Table 3 that the proposed Ga-U-Net model can also outperform several other network models on the HD95 index in the tumor core region, and its overall average index is better than the other network models. The proposed approach thus can accomplish the task of boundary segmentation with higher accuracy in the above three regions. Figure 8 shows examples of the segmentation results obtained with the trained model on the BraTS 2021 dataset, with the coronal, sagittal, and horizontal planes in an order from top to bottom. From left to right are the T1 modality, the real label, and the segmentation results. From the segmentation results, it can be seen from Figure 8 that the segmentation results on the tumor core region are highly close to the ground truths with little amounts of error. However, in the enhanced tumor region (i.e., the white region), a certain amount of error exists in the coronal and sagittal planes. In addition, there is also a modest amount of error for the segmentation of the edema region (i.e., the light grey region). In general, the proposed approach is able to separate the tumor from the healthy region, and it retains more complete information from the tumor. It is clear from Table 4 that the average Dice coefficient is slightly improved by introducing the GA module in the first layer of the encoder, as can also be seen from HD95, which is an overall improvement over the underlying network, whereas the effect instead slips more when the GA module is introduced in each layer of the encoder, proving that Gabor convolution indeed performs better in the low-level decoder and can achieve better results. In addition, when the CBAM attention mechanism is added to the network, the segmentation performance is also improved for both the 1 GA module and 4 GA modules. It is because the traditional attention mechanism based on convolutional neural network mainly focuses on the analysis of the channel domain and is limited to the descriptions of the interactions between the channels of the feature map. In contrast, the CBAM mechanism introduces two analysis dimensions, spatial attention and channel attention.