Traditional methods are difficult to achieve high-precision segmentation of brain tumor images,while manual image segmentation is time-consuming and laborious.Therefore,an improved UNet based brain tumor image segmentation algorithm is proposed.Firstly,embedding attention mechanism in the upsampling part of the model to improve the weight of the main features.Secondly,using transfer learning to improve the model's generalization ability.Finally,conduct experimental analysis.The experimental results show that the algorithm has better performance in brain tumor image segmentation.
维普
万方数据
