# New Approach for Brain Tumor Segmentation Based on Gabor Convolution and Attention Mechanism
## 1 Introduction  
With the rapid development of medical imaging technology, brain tumor image segmentation has made remarkable progress as an important medical image processing task. So far, two major types of brain tumors have been identified, including the primary and metastatic tumors based on whether the brain tumor cells are generated autonomously or migrate to the brain for generation [1]. Primary brain tumors originate in the brain tissue, while metastatic brain tumors are metastases of tumors from other sites. By histological type, they can be classified as glioma (G), meningioma (M), and pituitary tumor (P) [2], of which glioma of the brain is the most commonly seen type of primary tumor with a high mortality rate. Therefore, in order to treat these patients and extend their life expectancy, doctors usually need to analyze and develop accurate and reasonable treatment plans based on their brain images, and the most commonly used technique is magnetic resonance imaging (MRI) [3]. MRI can provide four different modalities, which are complementary to each other, and the combination of which can generate more detailed and comprehensive information about the structure and function of the brain. These four modalities complement each other and can be used in combination to provide more detailed and comprehensive information about brain structure and lesions, and such information can help doctors make more accurate diagnoses and develop more effective treatment plans [4]. MRI images are of significant value in diagnosing early brain tumor conditions. However, at present, in clinical practice, the process mainly relies on the experience of radiologists to determine the category of the tumor and manually mark the location of the brain tumor. This method not only wastes a large amount of human and material resources butalso may lead to misjudgments and omissions due to the subjective judgment of experts. Therefore, there is an urgent need for the availability of computer programs that can assist doctors in the classification and segmentation labeling of brain tumors. Such computer programs will help doctors focus more on developing individualized treatment plans and reduce their work pressure. When facing these tumors with irregular shapes and complex boundaries, manual segmentation is both a waste of time and prone to errors; scientists have thus been developing methods that can automatically segment brain tumors with high accuracy in recent years. Traditional methods include segmentation methods based on thresholds, boundaries, and regions [5], but the accuracy of these methods is not very high. In recent years, with the rapid development in the field of artificial intelligence, the integration of artificial intelligence into applications in the various fields has become increasingly important [6; 7; 8]. The application of automated computer-aided technology to the detection of brain tumor patients not only improves the efficiency of diagnosis but also reduces the work pressure of doctors. In brain tumor segmentation, compared with the inefficiency and high error of traditional methods, deep learning-based techniques have significantly improved the recognition rates of brain tumors and the segmentation accuracy of their tissues by automatically extracting features from MRI images. However, the development of highly accurate segmentation algorithms remains an important challenge to further improve the accuracy and robustness of brain tumor diagnosis. [PERSON] et al. [8] proposed the FCN architecture in 2015; different from the traditional CNN architecture, it replaces the fully connected structure with convolutional layers and adopts an up-sampling technology, which can obtain a segmented image with the same size as the original image. Compared with the traditional convolutional network, FCN effectively reduces the computation time and improves the segmentation accuracy. Therefore, FCN has become a pioneering work for the field of semantic segmentation in deep learning.