DeepNeXt: a lightweight polyp segmentation algorithm based on multi-scale attention.

ABSTRACT:

Background

Fast and accurate automatic segmentation of polyps in colonoscopy plays a crucial role in the early diagnosis and treatment of colon cancer. However, the current polyp segmentation algorithms based on deep neural networks suffer from the problems of larger models and lower segmentation accuracy. Meanwhile, achieving accurate segmentation of polyps is to improve the diagnostic efficiency of doctors, and this need motivates us to develop a set of lightweight models so that it can be easily embedded in clinical devices to meet the requirements of practical applications. This study aims to provide effective technical support for the rapid and precise segmentation of polyps in clinical applications.

Methods

DeepNeXt, an innovative polyp segmentation model grounded in m

SUBMITTER: Wang C 

PROVIDER: S-EPMC11652027 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature