earticle

영어

In recent years, UNet architecture has shown to be a standard network for medical image segmentation. However, it suffers from some severe limitations. It loses localization ability for low-level details followed by the inability of long-range dependencies. Motivated by this, we explore transformer-based architectures that exploit global context by modeling long-range spatial dependencies, which are essential for accurate polyp segmentation. In this paper, we propose an attention-based transformer encoded UNet model. This hybrid model inherits both characteristics of CNN block as well as attention block. We perform various experiments in existing architectures like UNet, ResUNet, ResUNet-Mod and our proposed method. The proposed method achieved a 0.645 mIOU score took an unassailable lead over prior methods.