Polyp Segmentation Using UNet and ENet

This paper presents a comprehensive study into the field of polyp segmentation using the novel use of two renowned deep learning architectures: U-Net and ENet. The paper, which focuses on the essential issue of identifying polyp areas in medical imaging, defines a distinct application of both U-Net and ENet algorithms, followed by a careful comparison of their various results. The study examines each algorithm’s unique strengths, limitations, and overall effectiveness and analyzing the segmentation data acquired from each method. Essentially, this study provides a thorough examination of polyp segmentation utilizing U-Net and ENet, opening the door for improved medical image analysis and informed decision-making in clinical terms.