Welcome to the GitHub repository of OrthoMate, a web application developed using HTML, CSS, JavaScript, and the Flask framework. OrthoMate is designed for knee osteoarthritis detection and severity prediction, utilizing the power of Convolutional Neural Networks (CNN) for image analysis.

OrthoMate is a state-of-the-art web application that aids in the early detection and severity prediction of knee osteoarthritis. By incorporating deep learning algorithms and machine learning techniques, OrthoMate assists healthcare professionals and patients in making informed decisions and personalized treatment plans.

The dataset used for training and evaluating OrthoMate contains a total of 1,656 images, categorized into five classes: Moderate, Mild, Severe, Normal, and Doubtful. The dataset can be accessed from the following link: OrthoMate Dataset.

• Knee Osteoarthritis Detection: OrthoMate uses Convolutional Neural Networks (CNN) to detect knee osteoarthritis from medical images, allowing for early diagnosis and intervention.
• Severity Prediction: The application utilizes machine learning algorithms to predict the severity of knee osteoarthritis, enabling clinicians to assess the condition's progression and recommend appropriate treatment strategies.
• Web-based Interface: OrthoMate offers a user-friendly web interface, making it accessible to medical professionals and patients from any device with an internet connection.
• Efficient and Accurate: The CNN-based approach ensures high accuracy and efficiency in analyzing medical data for osteoarthritis detection and severity prediction.

OrthoMate is built using the following technologies:

• HTML, CSS, JavaScript: The core web technologies used for creating the user interface and frontend of OrthoMate.
• Flask Framework: Flask is employed as the backend framework for handling server-side functionalities and routing.
• Convolutional Neural Networks (CNN): CNNs are utilized for image analysis and osteoarthritis detection from medical images.

Feel free to explore, fork, and submit pull requests to make OrthoMate an even more powerful tool for knee osteoarthritis detection and severity prediction.

• The following python modules must be installed to run the Application.
  • flask
  • TensorFlow
  • Keras
  • OpenCV

1. Run app.py to start the web application.

python app.py

2. Open the browser and went to the started server.
3. Now upload the image and see the result.

Thank you for your interest in OrthoMate! I believe that the fusion of web technologies, Flask framework, and CNN algorithm, along with the provided dataset, can bring substantial advancements in knee osteoarthritis management and improve patients' quality of life. Together, let's revolutionize osteoarthritis diagnosis and treatment. 🦴🔍