The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning
Not on the map. Why?
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%.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Oct 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
5 papers.
- 2023GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detectioncited by 20
- 2024Classification of Endoscopy and Video Capsule Images using CNN-Transformer Modelcited by 3
- 2023Vision Transformer for Efficient Chest X-ray and Gastrointestinal Image Classificationcited by 2
- 2021Deep ensembles in bioimage segmentationcited by 1
- 2026Abnormalities and Disease Detection in Gastro-Intestinal Tract Imagescited by 0
- 2026Abnormalities and Disease Detection in Gastro-Intestinal Tract Images23 Mar 2026
- 2024Classification of Endoscopy and Video Capsule Images using CNN-Transformer Model20 Aug 2024
- 2023GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection16 Jul 2023
- 2023Vision Transformer for Efficient Chest X-ray and Gastrointestinal Image Classification23 Apr 2023
- 2021Deep ensembles in bioimage segmentation24 Dec 2021