Pritesh K 
X-ray Body Part Classifier – Quick Demo using AI
🧩 Problem Statement
In hospitals, thousands of X-ray images are generated daily but many are:
- Unlabeled or misfiled
- Mixed in storage without body-part tags
This makes it hard for doctors and AI systems to automatically route or diagnose these images efficiently.
❓ Why Are We Solving This?
Automatically identifying which body part an X-ray image belongs to can:
- Help organize and sort large medical datasets
- Serve as a preprocessing step for disease detection models
- Reduce human effort in data labeling
- Allow smart routing of images to relevant specialists
Approach
We are building a lightweight AI model that:
- Takes any X-ray image as input
- Predicts the body part shown (e.g., wrist, shoulder, humerus)
- Displays the prediction in a simple UI
Dataset
For this demo, we used a subset of the MURA dataset (Stanford):
- 7 body part classes: elbow, finger, forearm, hand, humerus, shoulder, wrist
- 200 images per class to keep training fast
Structure:
data/ └── train/ ├── elbow/ ├── hand/ ├── shoulder/ └── …
Public datasets:
Tech Stack
| Component | Tool |
|---|---|
| Deep Learning Framework | PyTorch |
| Model | Pretrained ResNet18 |
| Frontend UI | Streamlit |
| Image Processing | Pillow, TorchVision |
| Deployment | Local browser via streamlit run |
Working
- Training a ResNet18 on your labeled X-ray images
- Saving the model and class labels
- Using Streamlit to create a web app:
- Upload an X-ray image
- View predicted body part and confidence scores
Output
xray_bodypart_model.pth– the trained model- Live web app to classify uploaded X-rays by body part
