Comparison of U-Net’s Variants for Segmentation of Polyp Images

Abstract

Medical image analysis involves examining pictures acquired by medical imaging technologies in order to address clinical issues. The aim is to increase the quality of clinical diagnosis and extract useful information. Automatic segmentation based on deep learning (DL) techniques has gained popularity recently. In contrast to the conventional manual learning method, a neural network can now automatically learn image features. One of the most crucial convolutional neural network (CNN) semantic segmentation frameworks is U-net. It is frequently used for classification, anatomical segmentation, and lesion segmentation in the field of medical image analysis. This network framework's benefit is that it not only effectively processes and objectively evaluates medical images, properly segments the desired feature target, and helps to increase the accuracy of medical image-based diagnosis.