I built and trained the COVID-Net model using a DenseNet-121 backbone, fine-tuning it for detecting COVID-19, pneumonia, and normal cases from chest X-ray images. Once the model was ready, I packaged everything into a clean, interactive Streamlit app that lets users upload an image and get an instant analysis with confidence scores and medical recommendations. After testing it locally, I pushed all the code—including the model weights, preprocessing scripts, and the Streamlit app—to a GitHub repository for version control and collaboration. Then, I went ahead and deployed the app on Streamlit Community Cloud so it’s live and accessible online. Finally, I embedded the live app into my WordPress site, making the AI-powered COVID-19 detector available to a wider audience right through the browser.
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 the COVID-Net model using DenseNet-121 for 4-class classification (COVID-19, Viral Pneumonia, Bacterial Pneumonia, Normal)
Developed a Streamlit web application for user interaction and real-time prediction
Uploaded all project files—model, app, and setup scripts—to GitHub
Deployed the app on Streamlit Cloud for public access
Embedded the live Streamlit app into a 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.