it is about interface
it is in roman language thaty is showing our previous test
it is a result of pneumonia x_ray
it is result interface that is in french
it is in roman language interface
it is a major interface
it is a home interface in french

Inspiration

I wanted to use my machine learning skills to solve a real-world problem. This project gave me an opportunity to explore how AI can help with chest X-ray image analysis.

What it does

X-RayAI classifies chest X-ray images into three categories: COVID-19, Pneumonia, and Normal. It also checks whether an uploaded image is an X-ray before making a prediction.

How we built it

I used Python, Pandas, and Scikit-learn to prepare the image dataset and train a Random Forest model. I built the backend using FastAPI and connected it to a web frontend. I also trained a separate image validation model.

Challenges we ran into

Preparing image data, training the models, connecting the frontend and backend, and deploying the application were the main challenges.

Accomplishments that we're proud of

I successfully built a working web application, achieved approximately 94.6% model accuracy during testing, and added a separate image validation feature.

What we learned

I gained practical experience in data preprocessing, machine learning, model evaluation, API development, and deployment. I also learned how to connect different technologies into one working project.

What's next for X-Ray AI

I plan to improve the model, test it on more diverse datasets, enhance the user interface, and make the application more reliable for educational and research purposes.