The proposed P-SwinNet model in this study addresses the significant impact of CRC, a leading cause of cancer-related deaths globally. By utilizing automatic segmentation and detection of colorectal polyps in colonoscopy videos, this technology aims to enhance the identification and diagnosis of colorectal diseases, potentially reducing the severity of colon cancer and improving patient QoL. P-SwinNet, a self-supervised transformer architecture, demonstrates promising results in polyps segmentation. This research showcases the potential of advanced computer vision techniques and self-supervised learning strategies in improving the accuracy and efficiency of colorectal polyps detection, contributing to better patient outcomes and potentially reducing cancer-related mortality rates.

According to WHO reports, cancer is the leading cause of death worldwide. The second most prevalent cause of cancer-related death in both men and women is colorectal cancer (CRC). One potential approach for reducing the severity of colon cancer is to utilize automatic segmentation and detection of colorectal polyps in colonoscopy videos. This technology can assist endoscopists in quickly identifying colorectal disease, leading to earlier intervention and better patient Quality of Life (QoL). In this article, we propose a self-supervised transformer based dual encoder–decoder architecture named P-SwinNet for polyps segmentation in colonoscopy images. The P-SwinNet adapts the dual encoder–decoder type of model to enhance the feature maps by sharing multiscale information from the encoder to the decoder. The proposed model uses multiple dilated convolutions to enlarge the field of view to gather more information without increasing the computational cost and the loss of spatial information...