Osteoporosis detection

This project leverages AI-powered deep learning to detect osteoporosis from X-ray images, providing a fast, cost-effective, and accessible alternative to traditional DXA scans. Using a Convolutional Neural Network (CNN), the system classifies bone X-rays into Normal, Osteopenia, or Osteoporosis, enabling early diagnosis and intervention. The dataset is preprocessed through image resizing, grayscale conversion, and normalization, and the trained model achieves an accuracy of ~85%. A user-friendly Streamlit web app allows users to upload X-ray images and receive instant AI-based risk assessment. This project has the potential to improve early osteoporosis screening, reduce fractures, and enhance patient care with future advancements like pretrained models, explainable AI, and cloud deployment.