A deep learning model for classifying the severity of osteoarthritis in knee joints from X-ray images. Built using PyTorch, this model uses Convolutional Neural Networks (CNN) to classify knee X-rays into five distinct grades, offering automated arthritis detection for clinical applications.
This repository contains a machine learning model that classifies knee X-ray images into one of five categories, each corresponding to a different severity of osteoarthritis:
- Grade 0: Healthy Knee
- Grade 1: Doubtful
- Grade 2: Minimal Osteoarthritis
- Grade 3: Moderate Osteoarthritis
- Grade 4: Severe Osteoarthritis
The model helps healthcare professionals in early detection and personalized treatment planning by automatically assessing the grade of arthritis based on X-ray images of knee joints.
The model classifies knee X-rays into the following 5 categories:
| Label | Category |
|---|---|
0 |
Healthy knee (Grade 0) |
1 |
Doubtful (Grade 1) |
2 |
Minimal osteoarthritis (Grade 2) |
3 |
Moderate osteoarthritis (Grade 3) |
4 |
Severe osteoarthritis (Grade 4) |
- Grade 0: Healthy knee with no signs of arthritis.
- Grade 1: Doubtful or early-stage arthritis, showing mild joint changes.
- Grade 2: Minimal osteoarthritis with clear but limited damage.
- Grade 3: Moderate osteoarthritis, with significant damage and pain.
- Grade 4: Severe osteoarthritis, characterized by extensive damage and chronic pain.
- Input: X-ray images of knee joints.
- Output: Arthritis grade (0-4).
- Framework: Built using PyTorch for deep learning, leveraging pre-trained models (e.g., ResNet, VGG) or custom CNN architecture.
- Model Performance: High accuracy in classifying X-ray images based on arthritis severity.
To use the trained model for inference:
- Load the trained model and input X-ray image.
- Preprocess the image (resize and normalize).
- Perform inference to predict the arthritis grade.
The model is evaluated using accuracy and other relevant metrics. It performs well in classifying knee X-rays into the appropriate arthritis grade, with results showing good separation between different grades.
This project is licensed under the MIT License - see the LICENSE file for details.