Automated MRI-Based Tumor Detection & Explainability
TumorTrack is a complete pipeline for detecting brain tumors from MRI scans using deep learning. It features:
- Multi-class classification (glioma, meningioma, pituitary, no-tumor) with state-of-the-art accuracy (~86%)
- Explainable AI visualizations (Grad‑CAM) for clinician-friendly interpretability
- Streamlit-powered UI to upload MRI scans and display predictions + heatmaps
- Robust CI/CD with GitHub Actions to lint, test, build Docker images, and run security scans (Trivy)
| Feature | Description |
|---|---|
| Deep Learning | Utilizes pre-trained CNN (VGG) fine-tuned on MRI datasets |
| Explainability | Generates Grad‑CAM heatmaps to highlight tumor regions |
| Dockerized Deployment | Run locally or in the cloud with a single docker run command |
| Automated CI/CD | Ensures code quality, security, and reproducible builds |
- Docker installed
- Optional: Python 3.11
docker pull jayesh422x/tumortrack:latest docker run -p 8501:8501 jayesh422x/tumortrack:latest
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template.