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Case study / Project archive
Bone Fracture Classification
An educational X-ray classification experiment with convolutional and transformer models.
Project archive
Historical project; AI-assisted development. Individual and team contribution has not been independently verified.
The idea
The problem
Explore convolutional and transformer-based X-ray classification as an educational comparison, with visual explanations and evaluation boundaries kept explicit.
The implementation
What I built
This preserves the educational image-classification research direction. It does not claim a reproduced fracture detector or an independently established personal implementation scope.
- Python
- PyTorch
- GradCAM
Features
- Archived educational intent: compare convolutional and transformer approaches to X-ray classification.
- The retained stack names GradCAM for visual inspection; a matching trained artifact and attribution output were not available for review.
Architecture
- The historical record describes a Python/PyTorch image-classification experiment with an explanation view.
- Image preprocessing, patient-level dataset partitions, model checkpoints, and evaluation code remain unverified without the matching source.
Validation and measured results
- No source inspection, model inference, patient-level leakage check, or held-out clinical evaluation was completed for this archived version.
Limits and unfinished work
- No diagnostic accuracy or clinical usefulness is claimed. A heatmap alone would not validate a model's medical reasoning.
- Older source can contain superseded descriptions. Numerical impact, adoption, awards, and deployment claims are not carried into this entry.
Evidence and sources
This project’s evidence remains local. Public source and downloadable receipts are not attached yet.