This project explores the practical use of the application of Deep Learning in medical image diagnosis.
This web application utilizes deep learning techniques to predict the likelihood of developing kidney disease. Built using Python, TensorFlow, Flask, MLflow, and DVC, the application provides a user-friendly interface for inputting patient data and receiving risk assessment results. An automated workflow seamlessly handles deployment and containerization, ensuring the application's accessibility and scalability.
The Fire Detection project offers the following features:
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Kidney Disease Prediction:
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User-friendly Interface:
To run this Fire Detection project locally, follow these steps:
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Clone the Repository:
https://github.com/Msparihar/Kidney-Disease-Prediction.git
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Install the required dependencies:
pip install -r requirements.txt
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Run the App:
python main.py
Contributions to the Fire Detection project are welcome! If you'd like to contribute, please follow these steps:
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Fork the repository on GitHub.
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Create a new branch from the
main
branch. -
Make your modifications and enhancements.
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Test your changes thoroughly.
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Commit and push your changes to your forked repository.
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Submit a pull request to the main repository, describing your changes in detail.
Please ensure your contributions adhere to the project's coding standards and guidelines.
The Fire Detection project is built upon various open-source libraries and resources. I would like to express my gratitude to the developers and contributors of the following projects:
This project is licensed under the MIT License. Feel free to modify and distribute it according to the terms of the license.
If you have any questions, suggestions, or feedback regarding this project, please contact the project maintainer at manishsparihar2020@gmail.com
I really appreciate your interest in this project and hope you found this project helpful! Keep Exploring!