Research On Brain Tumor Image Segmentation Based On CNN And Transformer

The purpose of brain tumor segmentation is to precisely locate and delineate the boundaries of brain tumors,which is crucial for the diagnosis,treatment planning,and disease monitoring.Traditional segmentation methods rely on manual operations by radiologists,which are not only time-consuming but may also lead to inconsistent results due to individual experiences and interpretation differences.Deep learning-based brain tumor segmentation is a method that utilizes advanced artificial intelligence technology to identify and segment tumor regions in brain scan images.These models are typically trained on a large number of annotated brain scan images to learn how to recognize tumors.Once trained,they can be used to quickly and consistently analyze new brain images,thereby improving the accuracy and efficiency of tumor detection.However,due to the specific characteristics of medical images,such as noise interference,data scarcity,the high complexity of target lesion areas,and data heterogeneity,achieving automatic and accurate segmentation of lesion areas by network models has become an extremely challenging problem.In addition,the application of deep learning in brain tumor segmentation also faces some challenges,such as the need for a large amount of high-quality training data,issues with algorithm interpretability,and the generalization ability among different machine learning models.This dissertation focuses on brain tumor image segmentation,with the following main contents and contributions:1.A new model named Multi-Feature Refinement and Aggregation(MRA)is proposed,aiming to improve the accuracy and efficiency of brain tumor image segmentation through deep learning technology.This model utilizes the design principles of residual and dense connections to construct a deep and efficient network structure,fully leveraging and integrating feature information across different layers through refinement and aggregation processes.By introducing two core modules,residual convolution and resolution fusion unit,it effectively integrates features of different resolutions,thereby enhancing the capability to recognize brain tumors and their surrounding complex tissue structures.The introduction of this model provides an effective strategy for hierarchical feature refinement and fusion in brain tumor segmentation,significantly improving the performance of segmentation tasks and thereby offering strong technical support for precision medicine.2.A novel image segmentation method combining transformer and convolutional technologies is proposed to address the issue of inaccurate localization in traditional transformer methods when dealing with image segmentation tasks.By introducing a scale transformer and a refinement module,this method can refine features and enhance feature information utilization across different scales,effectively mitigating the loss of detail information during the downsampling process.Moreover,this method employs a fuzzy selector to optimize the segmentation results,significantly enhancing the accuracy and robustness of segmentation by utilizing local spatial information and the regularity of training data.The design of the scale transformer not only achieves effective integration of local and global information but also optimizes information propagation through convolutional operations,providing an efficient and precise solution for complex area image segmentation.Experimental results demonstrate the significant advantages of this method in enhancing brain tumor segmentation performance.3.An innovative convolutional module is proposed,aimed at optimizing traditional convolutional neural networks through lightweight operations to enhance the efficiency and accuracy of brain tumor image segmentation.A multi-path structure is designed,effectively reducing the model’s computational complexity and parameter count while ensuring competitive segmentation results.Additionally,by integrating a symmetric network design and introducing a global attention channel mechanism,the feature extraction process is further refined,optimizing downsampling features,and enriching feature layers during the upsampling stage to achieve more accurate segmentation results.The proposed model demonstrates excellent computational efficiency and high accuracy in brain tumor segmentation tasks while significantly reducing model parameters.This dissertation explores the application of deep learning and artificial intelligence technologies,proposing three novel brain tumor image segmentation techniques.Initially,it identifies the challenges associated with multi-scale feature fusion and introduces a new model for refined and aggregated feature integration.Subsequently,it addresses the inaccuracies in localization found in traditional transformer methods during image segmentation tasks,as well as the issue of semantic feature loss in convolutional networks,by proposing a new image segmentation approach that combines transformer and convolutional techniques.Lastly,by reducing model parameters and enhancing computational efficiency,it demonstrates the potential for delivering high-performance medical services under resource-constrained conditions,providing a significant example for fostering continuous innovation and development in the healthcare sector.