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🩺 Project SWASTHYA

License: MIT Python

🚀 Overview

SWASTHYA (meaning health in Hindi) is an open-source Machine Learning & Deep Learning project for early detection and prediction of six common cancer types:

✔ Blood Cancer
✔ Breast Cancer
✔ Cervical Cancer
✔ Colorectal Cancer
✔ Gastric Cancer
✔ Lung Cancer

This project bundles ML/DL models, notebooks, and Streamlit apps to enable experimentation, training, and prediction tasks. Models are trained using tabular and/or image datasets achieving high performance (typically ~80–99% accuracy depending on the module).

⚠️ Note: This project is for research and educational purposes only and is not intended for clinical diagnosis.

📌 Features

  • Modular design with dedicated modules for each cancer type
  • Support for both tabular & image-based models
  • Pre-trained models and example notebooks
  • User-friendly Streamlit apps for quick predictions
  • GPU/TPU compatible workflows

🧠 Cancer Modules & Algorithms

Cancer Type Data Modality Model(s) Used
Blood Cancer Image CNN (EfficientNetB0)
Breast Cancer Tabular XGBoost, RandomForest
Image CNN (TPU optimized)
Cervical Cancer Tabular Random Forest
Colorectal Cancer Image CNN (ResNet50)
Tabular XGBoost + SMOTE
Gastric Cancer Image CNN (EfficientNetB0)
Lung Cancer Tabular XGBoost + RFE
Image CNN (VGG16)

Models achieve varied accuracy depending on data used and training configurations.

🛠️ Tech Stack

✔ Python
✔ TensorFlow / Keras
✔ Scikit-learn
✔ XGBoost
✔ Streamlit
✔ Pandas, NumPy, OpenCV
✔ Matplotlib, Seaborn

🗂️ Getting Started

1. Clone the Repository

git clone https://github.com/ANUBprad/Project-SWASTHYA.git
cd Project-SWASTHYA
  1. Install Dependencies
pip install -r requirements.txt

Make sure you have Python 3.7 or newer installed.

  1. Organize Your Data

Download the required datasets (e.g., from Kaggle) and place them in a structured folder like:

/data
  /blood_cancer
  /breast_cancer
  ...
  1. Train or Evaluate Models

Each module has a Jupyter notebook that demonstrates training or evaluation:

jupyter notebook blood_cancer_detection.ipynb

Follow similar steps for other modules.

  1. Run Streamlit App

Inside a module directory:

streamlit run app.py

📊 Performance Summary

Model performance varies across modules, typically achieving:

✔ ~80–99% accuracy ✔ ROC-AUC improvements with proper balancing & preprocessing

Metrics include:

Accuracy

ROC-AUC

Precision, Recall, F1-Score

Results depend on dataset, augmentation strategies, and training environment.

📦 Pretrained Models

Each module includes trained models (when available):

/models
  ├─ blood_cancer_detection_model.h5
  ├─ breast_tabular_model.joblib
  ├─ cervical_cancer_model.pkl
  ...

🤝 Contribution

Contributions are welcome! You can help by:

✔ Adding new cancer modules ✔ Improving model performance ✔ Enhancing documentation & examples ✔ Fixing bugs or improving UI

How to contribute:

Fork the repository

Create a feature branch

Commit your changes

Open a Pull Request

📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

📬 Contact

Maintained by ANUBprad Have questions? Open an issue or reach out on GitHub.