🩺 E2E Chest Cancer Detection

Welcome to the E2E Chest Cancer Detection repository!
This project delivers an end-to-end solution for detecting chest cancer from medical images using the power of deep learning and modern data science workflows.

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🚀 Project Overview

Chest cancer, including lung and other thoracic malignancies, remains one of the most critical health challenges worldwide. Early and accurate detection can significantly improve patient outcomes. This repository provides a robust, automated pipeline for:

• Preprocessing medical images
• Building and training deep learning models
• Evaluating model performance
• Making predictions on new data
• Visualizing results for interpretability

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✨ Key Features

• End-to-End Pipeline: From data preprocessing to prediction deployment.
• Deep Learning Models: Leverages state-of-the-art architectures (e.g., CNNs, transfer learning).
• User-Friendly: Clear, modular code and well-documented notebooks.
• Visualization Tools: Interpret model predictions with explainable AI techniques.
• Customizable: Easy to adapt for different datasets or similar medical imaging tasks.

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🛠️ Tech Stack

• Languages: Python
• Frameworks: PyTorch
• Libraries: NumPy, Pandas, OpenCV, Matplotlib, Seaborn, scikit-learn
• Utilities: Jupyter Notebook, FastAPI

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🧑‍💻 Getting Started

1. Clone the repo

   git clone https://github.com/Kra09-kp/E2EChestCancerDetection.git
   cd E2EChestCancerDetection

2. Install dependencies

   pip install -r requirements.txt

3. Prepare Data

   • Place your datasets in the data/ folder.
   • Modify paths as needed in the scripts or notebooks.

4. Run the Pipeline

   • Explore the Jupyter notebooks in the notebooks/ directory for EDA, training, and evaluation.
   • Use src/train.py to train your custom models.

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📊 Example Results

Add example images or performance metrics here!

• Accuracy: ...
• AUC-ROC: ...
• Sample Prediction Visualization:
  

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🔍 Interpretability

To help clinicians and data scientists trust the predictions, we include:

• Feature importance analysis
• Grad-CAM or saliency map visualizations

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📄 License

This project is licensed under the MIT License.

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💬 Contact

Created with ❤️ by Kra09-kp
For questions or collaborations, open an issue or reach out via GitHub!

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Disclaimer:
This project is for educational and research purposes.
Not for clinical use without proper validation and regulatory approval.