Data · dataset · 2026
Gastrointestinal-Disease-Detection
Listed in Teesside University Research Data Repository
Total Images: 1,196 endoscopic images Datasets: 2 combined sources Format: JPEG images with corresponding segmentation masks 1.
Description
Kvasir-SEG Dataset Images: 1,000 endoscopic images Structure: images/ - Original endoscopic images masks/ - Pixel-level segmentation masks (1,000 masks) kavsir_bboxes.json - Bounding box annotations in JSON format Features: Variable resolutions (typically 500-1400px width, 400-1100px height) Multiple polyps per image possible Bounding box coordinates: [xmin, ymin, xmax, ymax] Labels: "polyp" annotations File sizes: ~25KB to ~200KB per image Annotation Format: { "image_id": { "height": 529, "width": 622, "bbox": [ {"label": "polyp", "xmin": 38, "ymin": 5, "xmax": 430, "ymax": 338} ] }} 2.
Kvasir-Sessile Dataset Images: 196 endoscopic images Structure: images/ - Original endoscopic images masks/ - Segmentation masks (196 masks) Focus: Sessile (flat) polyp detection Purpose: Complementary training data for enhanced polyp detection Dataset Details CSV File The dataset_details.csv contains metadata for all 1,196 images: Columns: dataset - Source dataset (kvasir-seg or kvasir-sessile) image_filename - Image file name image_path - Full path to image mask_path - Full path to corresponding mask image_width - Image width in pixels image_height - Image height in pixels image_size_bytes - Image file size mask_size_bytes - Mask file size has_mask - Whether mask exists (Yes/No) num_bounding_boxes - Number of bounding boxes per image bounding_boxes - Bounding box coordinates (for kvasir-seg) label - Polyp label bbox_width / bbox_height - Bounding box dimensions Dataset Characteristics Image Resolutions: Common sizes: 528×622, 530×622, 546×626, 531×570 pixels High-resolution examples: 1070×1348, 1011×1220 pixels Standardized to 512×512 for training (as per project documentation) Polyp Distribution: Most images contain 1 polyp Some images contain multiple polyps (2-11 bounding boxes) All images are labeled as "polyp" (positive cases) Annotation Types: Segmentation Masks - Pixel-level polyp regions (binary masks) Bounding Boxes - Rectangular coordinates (kvasir-seg only) Labels - Polyp classification Usage in
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Project
Training Split: 70% (837 images) Validation Split: 15% (179 images) Test Split: 15% (180 images) Classes: Binary classification (Polyp vs. No Polyp) Applications: Deep learning model training (YOLO11m, CNN classifiers) Polyp detection and segmentation Clinical research in gastroenterology Dataset Purpose Designed for automated gastrointestinal polyp detection in endoscopic images, supporting: Computer vision research Medical AI development Clinical decision support systems Polyp detection and segmentation model training The dataset provides both pixel-level segmentation masks and bounding box annotations, suitable for both segmentation and detection tasks
Links
Where it is published
- DOI doi.org/10.17632/ts94gkj79z.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Earth & Environmental Science · Medicine & Health
- Inferred from text
- Image 75%
Provenance · 1 source records, 8 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/ts94gkj79z.1 | 16 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |