Image segmentation is considered necessary stage for a wide range of machine vision, artificial intelligence and medical imaging problems for meaningful analysis and interpretation of the acquired medical images. The task of automatic polyp segmentation is a long-standing challenge in biomedicine domain. The main goal of polyp segmentation is prevention of colorectal cancer which is useful for early polyp diagnosis. Methods that use deep learning approaches are rapidly becoming SOTA for majority of the automated polyp segmentation tasks in recent times. The ability of deep learning models in obtaining ordered feature maps is the foremost reason behind these advancements. Polyp segmentation of an image into different regions helps to detect disease region for early diagnosis. In this article, a novel encoder–decoder-based segmentation architecture has been proposed to identify distinguishing features that can be used to precisely separate the polyps. To address the challenge of poor con...

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 i...