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Artificial Intelligence · Major Project
Gastric Cancer Detection from Endoscopy Images
Flags abnormal findings in upper-GI endoscopy frames with a CNN
3-4 weeks build
Overview
Classifies endoscopy frames into normal and abnormal findings using public GI datasets such as Kvasir, as a proxy for gastric lesion screening. Frames are cleaned of text overlays and black borders, a pretrained CNN is fine-tuned, and a viewer highlights the regions behind each prediction.
What makes it stand up
- 01Public GI endoscopy images from the Kvasir collection
- 02Frame cleanup that masks overlays and instrument borders
- 03Transfer learning with per-class metrics and a confusion matrix
- 04Grad-CAM heatmaps for every prediction
- 05Academic screening prototype, not for clinical endoscopy use
Module breakdown
- 01
Dataset
Class selection, cleaning and stratified splits.
- 02
Preprocessing
Border masking, overlay removal and colour normalisation.
- 03
Model
Pretrained CNN fine-tuned for endoscopic findings.
- 04
Explainability
Heatmaps over mucosal regions of interest.
- 05
Viewer
Upload single frames or short clips for scoring.
After this, you will be able to
- Explain the domain gaps between endoscopy and natural images
- Defend your dataset choice and its limits as a proxy
- Demo scoring and heatmaps on unseen frames
Technology stack
- Python
- PyTorch
- OpenCV
- FastAPI
- React
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 3-4 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support