This project is an End-to-End Deep Learning implementation to classify chest CT-scan images as either normal or having signs of adenocarcinoma cancer. The project integrates MLOps tools like MLflow and DVC for efficient version control, tracking experiments, and CICD deployment.
Features:
- Deep Learning Model: Classifies chest CT-scans.
- MLOps Integration: Uses MLflow and DVC for experiment tracking and data versioning.
- CI/CD Pipeline: Ensures smooth deployment of the model.
- Web Components (Planned): Added structure for React and Django for potential future use.
Tools Used:
- MLflow: To track experiments and log models.
- DVC: To handle large datasets and track data versions.
- TensorFlow & Keras: For building and training the deep learning model.
- React (Planned): For the frontend user interface.
- Django (Planned): For the backend and API management.