# The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning

arXiv: https://arxiv.org/abs/1810.13278 · HTML: https://mlsanity.com/paper/1810.13278/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2018.

## Built on by (most cited first, 5 of 5)

- [GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection](https://mlsanity.com/paper/2307.08140/) (2023)
- [Classification of Endoscopy and Video Capsule Images using CNN-Transformer Model](https://mlsanity.com/paper/2408.10733/) (2024)
- [Vision Transformer for Efficient Chest X-ray and Gastrointestinal Image Classification](https://mlsanity.com/paper/2304.11529/) (2023)
- [Deep ensembles in bioimage segmentation](https://mlsanity.com/paper/2112.12955/) (2021)
- [Abnormalities and Disease Detection in Gastro-Intestinal Tract Images](https://mlsanity.com/paper/2603.22378/) (2026)

## Abstract (arXiv)

In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract. We have proposed a system based on global features and deep neural networks. The best approach combines two neural networks, and the reproducible experimental results signify the efficiency of the proposed model with an accuracy rate of 95.80%, a precision of 95.87%, and an F1-score of 95.80%.