Medical Imaging

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.

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❓ 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

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🧠 Approach

We are building a lightweight AI model that:

1. Takes any X-ray image as input
2. Predicts the body part shown (e.g., chest, hand, knee)
3. Displays the prediction in a simple UI

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🗃️ Dataset

For the demo testing, we use a small custom sample dataset containing:

• Chest X-rays
• Hand X-rays
• (You can add more like knee, pelvis, skull)

We structure the dataset as: data/ train/ chest/ chest1.png hand/ hand1.png

Public datasets like:

• MURA Dataset (Stanford)
• VinDr X-ray Dataset (Kaggle)

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⚙️ Tech Stack

Component	Tool
Deep Learning Framework	PyTorch
Model	Pretrained ResNet18
Frontend UI	Streamlit
Image Processing	Pillow, TorchVision
Deployment	Local browser via streamlit run

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🚀 How It Works

1. Train a simple ResNet18 on your labeled X-ray images
2. Save the model and class labels
3. Use Streamlit to build a web interface:
   • Upload an image
   • View predicted body part + confidence score

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✅ Output

• A trained model file: xray_bodypart_model.pth
• A live web app that classifies uploaded X-ray images by body part

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📦 How to Run (Quick Start)

# Install dependencies
pip install torch torchvision streamlit pillow

# Train the model
python train.py

# Run the UI
streamlit run app.py