AG-ResUNet++: An Improved Encoder-Decoder Based Method for Polyp Segmentation in Colonoscopy Images

Abstract

Colorectal cancer is one of the most prevalent causes of cancer-related death. Early polyp segmentation in colonoscopy is helpful in diagnosing and preventing colorectal cancer. However, this task a challenging due to variations in the appearance of polyps. This paper proposes a new encoder-decoder-based method called AG-ResUNet++ that leverages attention gate mechanism and residual connections to enhance the performance of the existing UNet++ model in the polyp segmentation task. Our method considerably outperforms other state-of-the-art methods on the popular polyp segmentation datasets, including KvasirSEG and CVC-612.