DiaCure is a machine learning project aimed at early detection of diabetes using retinal images. This approach leverages advanced algorithms and optimized neural networks to analyze retinal features for predicting diabetic status.
- Retinal Image Analysis: Utilizes a diverse dataset of retinal images.
- Optimized Neural Networks: Implements nature-inspired algorithms (e.g., Genetic Algorithms, Particle Swarm Optimization) for enhanced model performance.
- User-Friendly Interface: Designed for healthcare professionals with an emphasis on interpretability.
- Python 3.x
- Jupyter Notebook
- Required libraries:
pip install pandas numpy scikit-learn matplotlib Flask React
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Clone the Repository
git clone https://github.com/suhass434/Diacure-website.git cd DiaCure -
Data Preparation
- Gather a dataset of retinal images labeled with diabetic and non-diabetic statuses.
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Run the Jupyter Notebook
- Open Jupyter Notebook and load
diacure_model.ipynb. - Follow the instructions within the notebook to preprocess images and train the model.
- Open Jupyter Notebook and load
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Deploy the Interface
- Run the Flask backend to serve the model.
- Use React for the frontend interface to interact with the model.
Feel free to modify any sections to better fit your needs!