AFANet: Adaptive feature aggregation for polyp segmentation

In terms of speed and accuracy, the deep learning-based polyp segmentation method is superior. It is essential for the early detection and treatment of colorectal cancer and has the potential to greatly reduce the disease's overall prevalence. Due to the various forms and sizes of polyps, as well as the blurring of the boundaries between the polyp region and the surrounding mucus, most existing algorithms are unable to provide highly accurate colorectal polyp segmentation. Therefore, to overcome these obstacles, we propose an adaptive feature aggregation network (AFANet). It contains two main modules: the Multi-modal Balancing Attention Module (MMBA) and the Global Context Module (GCM). The MMBA extracts improved local characteristics for inference by integrating local contextual information while paying attention to them in three regions: foreground, background, and border. The GCM takes global information from the top of the encoder and sends it to the decoder layer in order to further investigate global contextual feature information in the pathologic picture. Dice of 92.11 % and 94.76 % and MIoU of 91.07 % and 94.54 %, respectively, are achieved by comprehensive experimental validation of our proposed technique on two benchmark datasets, Kvasir-SEG and CVCClinicDB. The experimental results demonstrate that the strategy outperforms other cutting-edge approaches.