U-Net Medical Image Segmentation Based on Attention Mechanism Combination

The accuracy of medical image segmentation is of great significance to the diagnosis of patients. With the development of deep learning, the segmentation of medical images using convolutional neural networks has become a research hotspot. After the U-Net model was proposed, it has gradually become a commonly used convolutional neural network model in the field of medical image segmentation. However, medical images have the characteristics of different shapes of target organs and the image boundaries are not easy to determine. These problems lead to poor segmentation performance of the U-Net model. In view of the above problems, the attention mechanism is introduced from the two dimensions of space and channel to improve the U-Net model. Use Dice coefficient and IOU (Intersection Over Union) as evaluation metrics to compare model performance on multiple medical image datasets. The experimental results show that the U-Net model after introducing the attention mechanism has a better segmentation effect.