BreastCancer-9.mp4

README.md for your Project

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Breast Cancer Detection Using Kedro and Swin Transformer

Project Overview

This project aims to build a pipeline for breast cancer detection using Kedro and Swin Transformer for object detection and classification. The project is structured as a Kedro-based project, with additional components related to Swin Transformer for deep learning tasks.

Project Structure:

• Path: \breastCancer\python_project\kedro-introduction-tutorial-master
• Subproject: Swin_Transformer_Object_Detection_master for deep learning-based object detection using the Swin Transformer model.

Setup Instructions

Step 1: Setting Up the Environment

First, you need to set up the virtual environment (venv) and install all necessary dependencies. Follow the steps below.

Create and Activate Virtual Environment:

# Create the virtual environment named venv2
python -m venv venv2

# Activate the virtual environment
# On Windows:
venv2\Scripts\activate

# On Linux or MacOS:
source venv2/bin/activate

Step 2: Install Kedro and Requirements

Navigate to the main project directory and install the necessary dependencies.

cd \breastCancer\python_project\kedro-introduction-tutorial-master

# Install Kedro version 0.16.5 and additional dependencies
pip install kedro==0.16.5
pip install kedro[pandas]==0.16.5  # Pandas installed separately due to pip bug
pip install kedro-viz scipy matplotlib  # Additional requirements

Step 3: Install Swin Transformer Dependencies

The project utilizes Swin Transformer for object detection. To set up this module, navigate to the Swin_Transformer_Object_Detection_master subproject and install the relevant requirements.

cd sub_projects\Swin_Transformer_Object_Detection_master

# Install requirements from build.txt, docs.txt, and runtime.txt
pip install -r requirements\build.txt
pip install -r requirements\docs.txt
pip install -r requirements\runtime.txt

Once these steps are completed, you should have all necessary packages and libraries installed.

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About the Project

1. Kedro

Kedro is used to structure the pipeline for data processing, model training, and evaluation. Kedro helps in creating modular, scalable, and reproducible workflows.

2. Swin Transformer

Swin Transformer is a state-of-the-art model for object detection and image classification. It is utilized in this project for detecting cancerous regions from medical images.

3. Project Goals

• Create a scalable pipeline for breast cancer detection using deep learning models.
• Use Swin Transformer to enhance detection accuracy.
• Structure the workflow using Kedro for efficient data handling and model integration.

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Key Features

• Modular pipeline: The project is structured using Kedro to provide clear separation of concerns and enhance reproducibility.
• Swin Transformer for Detection: Integration of the Swin Transformer model for accurate object detection and classification of breast cancer images.
• Visualization: Use kedro-viz and matplotlib for visualizing pipeline components and model results.

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Running the Project

After setting up the environment and installing all requirements, you can run the project using the following commands:

1. Running laravel application:

   cd breastCancer
   php artisan serve

2. Running Kedro pipeline:

   kedro run

3. Visualizing the pipeline:

   kedro viz

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Learning Resources

1. Kedro Documentation
2. Swin Transformer GitHub Repo

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Contributing

If you'd like to contribute to this project, feel free to open a pull request or reach out with any suggestions.

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License

This project is licensed under the MIT License. See the LICENSE file for more details.