Knee-Osteoarthritis-Classification

Detection and Classification of Knee Osteoarthritis using Deep Learning Techniques

Screenshots

Introduction

This application classifies knee osteoarthritis severity based on X-ray images using a convolutional neural network (CNN) model. It follows the Kellgren-Lawrence grading scale and provides users with an easy-to-use interface via Streamlit.

Features

• Upload X-ray images for knee osteoarthritis classification
• Automatic grading based on the Kellgren-Lawrence scale
• Visualization of model predictions
• Simple and interactive UI using Streamlit

Tech Stack

Machine Learning:

• TensorFlow/Keras (for CNN model)
• OpenCV (for image preprocessing)
• NumPy & Pandas (for data handling)

Frontend:

• Streamlit (for UI)

Backend:

• FastAPI (optional, for API-based predictions)
• SQLite/PostgreSQL (optional, for storing user data and results)

Dataset:

https://www.kaggle.com/datasets/shashwatwork/knee-osteoarthritis-dataset-with-severity

Installation and Setup

Prerequisites:

Ensure you have the following installed:

• Python (>=3.8)
• pip or conda

Steps to Run the Project:

1. Clone the repository:

   git clone https://github.com/iamakashrout/Knee-Osteoarthritis-Classification.git
   cd Knee-Osteoarthritis-Classification

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Streamlit application:

   streamlit run app.py

4. Access the application: Open http://localhost:8501 in your browser.

Contributing

Feel free to fork the repo and submit pull requests. Make sure to follow coding standards and write clean, modular code.

Contact

For any issues, feel free to reach out via GitHub Issues.