AGCNet: A Precise Adaptive Global Context Network for Real-Time Colonoscopy

Colonic endoscopy is the gold standard for detecting rectal polyps and rectal cancer. In which polyps are a major predisposing factor for colorectal cancer, the precise diagnosis of polyps within colorectal endoscopy is highly dependent on a physician of professional level. With the development of deep learning, some semantic segmentation methods have recently been applied to polyp detection, but there are problems with insufficient accuracy and segmentation speed. To this end, we propose a precision adaptive global context network (AGCNet) based on real-time colon endoscopy. Firstly, in order to adapt to the problem of large-scale variation of polyps, we designed a multi-scale semantic fusion module (MSFM), which enhances the representation capability by varieties of filters to collect contextual information at different scales, thus adapting to the problem of large variation of polyp size, especially smaller polyps. In addition, modelling long-range dependence by simply using complex spatial pixels tends to introduce more background noise and increase the computational effort. To this end, a context-aware pyramid aggregation module (CPAM) was designed, which internally includes a novel dual attention mechanism whereby the CPAM aggregates feature information across different regions to boost the network’s ability to utilize global context and model long-range dependency through dual attention to further reinforce the features information of important regions and efficiently suppress features in non-important regions. Additionally, the CPAM performs multi-level pooling on the input features to extract multi-scale context information from the image and uses an attention mechanism to selectively highlight informative regions of the image that are most relevant to the segmentation task. The module fuses the multi-level pooled features with the attention map to produce enhanced feature representations that capture both global and local information. Thereby achieving precise polyp segmentation and taking real-time into account. Our proposed AGCNet performed extensive experimental studies on datasets Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB and ETIS-LaribPolypDB. Specifically, AGCNet achieved an IoU of 87.40% and a Dice score of 92.63% on the Kvasir dataset, achieving accurate segmentation results faster than many current state-of-the-art models.