# Refined Deep Neural Network and U-Net for Polyps Segmentation

Source: [Refined Deep Neural Network and U-Net for Polyps Segmentation](<https://www.sota2.com/research/paper/68fa6a0d1e209131cea25d04>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:15:37.395Z

Authors: Quoc-Huy Trinh, Minh-Van Nguyen, Thiet-Gia Huynh, Minh-Triet Tran

Published: 2021-05-31T10:02:52.000Z

## Abstract

The Medico: Multimedia Task 2020 focuses on developing an efficient and accurate computer-aided diagnosis system for automatic segmentation \[3\]. We participate in task 1, Polyps segmentation task, which is to develop algorithms for segmenting polyps on a comprehensive dataset. In this task, we propose methods combining Residual module, Inception module, Adaptive Convolutional neural network with U-Net model, and PraNet for semantic segmentation of various types of polyps in endoscopic images. We select 5 runs with different architecture and parameters in our methods. Our methods show potential results in accuracy and efficiency through multiple experiments, and our team is in the Top 3 best results with a Jaccard index of 0.765.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2105.14848>)

[PDF](<https://arxiv.org/pdf/2105.14848.pdf>)