Context Aware Automatic Polyp Segmentation Network With Mask Attention

Colorectal cancer stands out as a major factor in cancer-related fatalities. The prevention of colorectal cancer may be aided by early polyp diagnosis. Colonoscopy is a widely used procedure for the diagnosis of polyps, but it is highly dependent on the skills of the medical practitioner. Automatic polyp segmentation using computer-aided diagnosis can help medical practitioners detect even those polyps missed by humans, and this early detection of polyps can save precious human lives. Due to the lack of distinct edges, poor contrast between the foreground and background, and great variety of polyps, automatic segmentation of polyps is quite difficult. Although there are several deep learning-based strategies for segmenting polyps, typical convolutional neural network (CNN)-based algorithms lack long-range dependencies and lose spatial information because of consecutive convolution and pooling. In this research, a novel encoder–decoder-based segmentation architecture has been proposed in an effort to identify distinguishing features that can be used to precisely separate the polyps. The proposed architecture combines the strengths of a pretrained ResNet50 encoder, residual block, our proposed multiscale dilation block, and the mask attention block. Multiscale dilation block enables us to extract features at different scales for better feature representation. The mask attention block utilizes a generated auxiliary mask in order to concentrate on important image features. To evaluate the proposed architecture, several polyp segmentation datasets have been used. The obtained findings show that the suggested architecture performs better than several state-of-the-art (SOTA) approaches for segmenting the polyps.