ArticleData in brief2025
GastroEndoNet: Comprehensive endoscopy image dataset for GERD and polyp detection.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence in Gastrointestinal Endoscopy: Current Advances, Clinical Integration, and Future Directions: A Narrative Review.Clinical and experimental gastroenterology · 2026Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The gastrointestinal (GI) system is fundamental to human health, supporting digestion, nutrient absorption, and waste elimination. Disruptions in GI function, such as Gastroesophageal Reflux Disease (GERD) and gastrointestinal polyps, can lead to significant health complications if not diagnosed and managed early. However, manual interpretation of endoscopic images is time-consuming and prone to human error, highlighting the need for automated diagnostic tools. In this study, we introduce a comprehensive dataset of 24,036 high-quality endoscopic images, categorized into four classes: GERD, GERD Normal, Polyp, and Polyp Normal. This dataset is designed to facilitate research in automated detection and classification of these conditions through machine learning algorithms. The dataset consists of 4006 primary images collected following endoscopic procedures, which were augmented using six distinct techniques, expanding the total number of images to 24,036. It includes 5844 images of GERD cases (974primary images), 6618 images of GERD Normal (1103 primary images), 4674 images of Polyps (779 primary images), and 6900 images of Polyp Normal (1150 primary images). These images, pre-processed and resized to a resolution of 512 × 512 pixels, were obtained from Zainul Haque Sikder Women's Medical College & Hospital (Pvt.) Ltd. and saved in JPG format. This dataset addresses a critical gap in the availability of large, diverse, and well-labelled medical image datasets for training AI-driven healthcare solutions. It provides an invaluable resource for developing machine learning models aimed at the automatic diagnosis, classification, and detection of GERD and polyps, potentially improving the speed and accuracy of clinical decision-making. By leveraging this dataset, researchers can contribute to enhanced diagnostic tools that could significantly improve healthcare outcomes and patient quality of life in the field of gastroenterology.
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Registered trials
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