BetterNet: An Efficient CNN Architecture with Residual Learning and Attention for Precision Polyp Segmentation

arXiv:2405.04288v1 Announce Type: cross
Abstract: Colorectal cancer contributes significantly to cancer-related mortality. Timely identification and elimination of polyps through colonoscopy screening is crucial in order to decrease mortality rates. Accurately detecting polyps in colonoscopy images is difficult because of the differences in characteristics such as size, shape, texture, and similarity to surrounding tissues. Current deep-learning methods often face difficulties in capturing long-range connections necessary for segmentation. This research presents BetterNet, a convolutional neural network (CNN) architecture that combines residual learning …

architecture arxiv attention cnn cs.cv cs.lg eess.iv precision residual segmentation type