Improving Unet Networks for Medical Image Segmentation by adding Attention Mechanism Layers

Medical image segmentation is one of the most important steps in medical image analysis to improve diagnosis and findings. One of the most common segmentation methods in deep learning is the use of Unet networks. The presence of overlapping layers in the Unet decoder does not allow extracting information from deeper layers. Also, due to the limited range of the received field of convolution cores, long-range information and dependencies are not considered well. In this article, our goal is to place a structure in the area between encoder and decoder in the Unet model in order to fill the semantic gap between the encoder and decoder area and better extract features by paying attention to local and global features. This model makes the target region more prominent in different medical datasets. We have conducted our experiment on 6 medical data sets, and the results obtained in two evaluation criteria, Dice and Iou, show that our proposed model has better results than Unet and based methods.