Knee Arthritis Detection Notebook

This repository contains a Jupyter Notebook for detecting knee arthritis using machine learning techniques. The notebook demonstrates the complete workflow from data loading, preprocessing, model training, evaluation, to prediction.

Table of Contents

• Overview
• Dataset
• Prerequisites
• Installation
• Usage
• Results
• Contributing
• References

Overview

This notebook represents the application of Deep Learning for detecting the level of knee arthritis by a provided X-ray.

Dataset

The dataset used in this notebook is assumed to be pre-split into the 5 categories (Normal, Doubtful, Mild, Moderate, Severe).

Prerequisites

Before running the notebook, you need to have the following libraries installed:

• Python 3
• Jupyter Notebook
• Tensorflow
• Keras
• matplotlib
• numpy

Installation

To set up your environment, follow these steps:

1. Clone the repository:

   git clone https://github.com/akhundMurad/KneeArthritisDetection.git

2. Navigate to the project directory:

   cd KneeArthritisDetection

3. Install the required packages:

   pip install -r requirements.txt

Usage

To run the notebook, follow these steps:

1. Start Jupyter Notebook:

   jupyter notebook

2. Open KneeArthritisDetection.ipynb in your Jupyter environment.

3. Follow the steps in the notebook to execute each cell. Make sure to run the cells to avoid any errors.

Results

The notebook will output various results including data visualizations, model performance metrics, and predictions. Make sure to review the results section to understand the effectiveness of the model.

As a result of the experiment, I tuned the model to increase validation accuracy from 35% to 82%.

Contributing

Contributions are welcome! If you have any suggestions or improvements, please create a pull request or raise an issue.

References

• Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60, 84 - 90.
• Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. International Conference on Artificial Intelligence and Statistics.
• Antony, J., McGuinness, K., Moran, K., & O’Connor, N.E. (2017). Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity Using Convolutional Neural Networks. IAPR International Conference on Machine Learning and Data Mining in Pattern Recognition.
• Chollet, F. (2017). Deep learning with python. Manning Publications.