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

TL;DR

The document presents a LaTeX template bundle for Elsevier's CAS journals, introducing two class files: cas-sc.cls for single-column and cas-dc.cls for double-column layouts to streamline manuscript formatting and submission. It explains front matter customization, including the longmktitle option to accommodate lengthy titles and affiliations, as well as the organization of author marks, affiliations, and footnotes within the front matter. The guide also covers theorems, proofs, and other environments, providing example code snippets to facilitate advanced formatting. Overall, the template aims to simplify compliant manuscript preparation for Elsevier workflows and similar submission contexts by offering structured front matter, sample boilerplate, and clear usage instructions.

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 and attention methods to enhance the accuracy of polyp segmentation. The primary characteristics encompass (1) a residual decoder architecture that facilitates efficient gradient propagation and integration of multiscale features. (2) channel and spatial attention blocks within the decoder block to concentrate the learning process on the relevant areas of polyp regions. (3) Achieving state-of-the-art performance on polyp segmentation benchmarks while still ensuring computational efficiency. (4) Thorough ablation tests have been conducted to confirm the influence of architectural components. (5) The model code has been made available as open-source for further contribution. Extensive evaluations conducted on datasets such as Kvasir-SEG, CVC ClinicDB, Endoscene, EndoTect, and Kvasir-Sessile demonstrate that BetterNets outperforms current SOTA models in terms of segmentation accuracy by significant margins. The lightweight design enables real-time inference for various applications. BetterNet shows promise in integrating computer-assisted diagnosis techniques to enhance the detection of polyps and the early recognition of cancer. Link to the code: https://github.com/itsOwen/BetterNet