Deep Learning–Based Knee Osteoarthritis Prediction Using Convolutional Neural Networks

Description:

      This project focuses on the automatic prediction of Osteoarthritis from knee X-ray images using Deep Learning. Osteoarthritis is a common joint disorder that affects the knee and can lead to pain, stiffness, and reduced mobility. Early detection is important for proper treatment and management. In this system, a Convolutional Neural Network (CNN) model is trained to classify knee X-ray images into two categories: Normal and Osteoarthritis. The trained model learns important image features such as joint space narrowing and bone structure patterns to make accurate predictions.

      The implementation is developed using Python, PyTorch, and Django. The deep learning model is trained offline and saved as a .pth file. The Django web framework is then used to build a user-friendly web application where users can create an account, log in securely, and upload knee X-ray images for prediction. When an image is uploaded, it is preprocessed (resized, normalized, and converted into tensor format) and passed to the trained CNN model. The system then displays the predicted class along with the uploaded image on the webpage.

     Overall, this project demonstrates the integration of Artificial Intelligence and Web Technology to build a real-world medical image classification system. It combines deep learning for automated disease detection with a secure web-based interface for accessibility and ease of use. The system provides a simple, efficient, and automated solution that can assist in preliminary Osteoarthritis screening and support medical professionals in decision-making.

Input:

     Knee X-ray Image

Output:

     Predicted class (Normal / Osteoarthritis)

Tags:

#Osteoarthritis Prediction, #Medical Image Classification, #Deep Learning, #Convolutional Neural Network (CNN), #Knee X-ray Analysis, #Computer Vision in Healthcare, #Artificial Intelligence in Medicine, #Disease Detection System, #Binary Image Classification, #PyTorch, #Django Web Application, #Image Preprocessing, #Healthcare Technology, #Automated Diagnosis System, #Machine Learning in Medical Imaging, #Secure Login Authentication System, #Web-Based AI Application, #Clinical Decision Support System, #AI-Powered Healthcare Solutions.

Reference:

1. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

2. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems (NIPS).

3. Litjens, G., Kooi, T., Bejnordi, B. E., et al. (2017). A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis, 42, 60–88.