I started by building and training the core deep learning model, customizing a ResNet-50 architecture to specialize in analyzing X-ray images for bone fractures. Once the model was robust and ready, I packaged the entire project—including the model weights, processing scripts, and the Streamlit application—and uploaded it to GitHub for version control and sharing. The next step was deployment; I brought the application to life on Streamlit Community Cloud, making it publicly accessible as an interactive web tool. Finally, to integrate this AI capability directly into my professional portfolio, I embedded the live Streamlit app into my WordPress website, creating a seamless experience for visitors to test the fracture detector without leaving the page.
This system is designed not as a diagnostic tool but as a potential assistive technology, and its future evolution must be guided by close collaboration with healthcare professionals to ensure it meets clinical needs and integrates safely into existing workflows.
Key Tasks Completed:
Trained and fine-tuned a ResNet-50 model for bone fracture detection.
Uploaded the complete project source code and files to a GitHub repository.
Deployed the application as a live, public tool on Streamlit Community Cloud.
Embedded the deployed Streamlit app directly into my WordPress website.
⚠️ Attention
If you see the message “This app has gone to sleep due to inactivity”, it simply means the service we use is hosted on a free platform that automatically goes to sleep when there hasn’t been any activity.
Just click “Wake it up.”
After it loads, you will see the application shown in the image below.