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Output
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Training and Validation Loss
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Training and Validation Accuracy
Inspiration
Chest X-rays are widely used to help identify lung diseases, but interpreting them requires medical expertise and can take time. I wanted to explore how deep learning could assist in analyzing chest X-ray images and make AI-based medical image analysis more accessible.
What it does
AI-Powered Chest X-ray Disease Detection is a deep learning project that explores automated disease classification from chest X-ray images. The project includes model experimentation, saved model files, a backend, a dashboard, and evaluation visualizations to assess model performance.
How we built it
I organized the project into separate modules for datasets, model files, Jupyter notebooks, backend functionality, and the dashboard. I used Python and deep learning workflows to experiment with chest X-ray classification models, evaluate their performance, and visualize accuracy and loss. The project also includes test metrics to help assess model performance.
Challenges we ran into
Key challenges included working with medical image datasets, preparing and evaluating deep learning models, organizing the model and application components, and understanding how to assess classification performance reliably.
Accomplishments that we're proud of
Developed an end-to-end project structure for AI-powered chest X-ray disease detection, integrating datasets, trained model files, backend functionality, and a dashboard.
Experimented with deep learning models for medical image classification and evaluated their performance using accuracy, loss curves, and test metrics.
Organized the codebase into modular components to make experimentation, evaluation, and future improvements easier.
Gained practical experience in medical image analysis, model evaluation, and building an application around deep learning.
5.Established a foundation for future improvements in model reliability, usability, and accessibility.
What we learned
This project helped me gain practical experience in deep learning experimentation, medical image classification, model evaluation, Python development, and organizing an AI application into modular components.
What's next for AI-Powered Chest X-ray Disease Detection
I aim to improve model validation, strengthen the dashboard and backend, and explore ways to make the application more useful and accessible. The system is intended as an AI research and decision-support prototype, not a replacement for professional medical diagnosis.
Built With
- computer-vision
- deep-learning
- fastapi
- jupyter
- keras
- python
- streamlit
- tensorflow
