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Artificial Intelligence · Major Project
Hand Bone Joint Detection in X-Rays
Locates finger and wrist joints in hand radiographs
3-4 weeks build
Overview
Combines classical image processing with a deep detector to find joints in hand X-rays. CLAHE contrast enhancement and edge filtering prepare radiographs from a public dataset such as RSNA Bone Age, a YOLOv8 model detects each joint region, and the app crops joints for closer review.
What makes it stand up
- 01Hand radiographs from the public RSNA Bone Age collection
- 02CLAHE, denoising and edge enhancement before detection
- 03YOLOv8 joint detector with mAP and per-joint recall reported
- 04Joint cropping for downstream arthritis or bone-age study
- 05Academic research prototype, not a radiology tool
Module breakdown
- 01
Preprocessing
Contrast enhancement, denoising and orientation fixes.
- 02
Annotation
Joint bounding-box labelling guide and YOLO export.
- 03
Detector
YOLOv8 training with augmentation for X-ray variance.
- 04
Joint crops
Per-joint extraction with labels and coordinates.
- 05
Web app
Upload, annotated overlay and joint gallery.
After this, you will be able to
- Explain how CLAHE differs from global histogram equalisation
- Defend the value of classical preprocessing before deep learning
- Demo joint detection on a new hand radiograph
Technology stack
- Python
- YOLOv8
- 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