Brats-2018_2019-2D

Brain tumors are one of the most common diseases threatening human health. Early detection and precise segmentation are of great significance for clinical diagnosis and treatment. This paper presents a Learnable Wavelet Transform and Attention Mechanism network(LWTA-Net2D), based on 2D Convolutional Neural Networks (CNN), integrating Learnable Discrete Wavelet Transform (LDWT), combination of Monte Carlo Attention (MCattn) and Monte Carlo Bottleneck Layer (MCBottleneck), and a U-Net-based encoder-decoder architecture. By training and testing on 2D slices of the BRATS2018 and BRATS2019 datasets, the proposed model demonstrates superior performance in multi-scale feature capturing and spatial detail enhancement. Experimental results show that the improved model achieves Intersection over Union (IoU) and Dice Similarity Coefficient (Dice) of 0.7842 and 0.8766, respectively, representing a 5\% improvement over traditional U-Net methods, significantly enhancing segmentation accuracy and robustness. This study provides an efficient and precise solution for MRI brain tumor segmentation tasks, showing great potential in medical image processing.