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Neural Network Classification for Diabetic Retinopathy

Overview

This project aims to develop a neural network model for classifying diabetic retinopathy using a dataset from the UCI Machine Learning Repository. The project addresses class imbalance and employs advanced techniques to enhance model performance, including hyperparameter tuning and early stopping.

Project Structure

  • Data Preparation: Standardized and cleaned the dataset, handled class imbalance using SMOTE, and prepared data for training.
  • Neural Network Development: Constructed and trained a neural network with early stopping to optimize performance.
  • Performance Evaluation: Evaluated the model's accuracy and loss to ensure effectiveness.

Dataset

The dataset is sourced from the UCI Machine Learning Repository and includes features related to diabetic retinopathy, with a target variable indicating the presence of the condition.

Technologies Used

  • TensorFlow: For building and training the neural network model.
  • scikit-learn: For data preprocessing, splitting, and handling class imbalance.
  • imblearn: For applying SMOTE to address class imbalance.
  • Matplotlib & Seaborn: For data visualization.

Installation

To run this project, install the required Python libraries:

pip install numpy pandas matplotlib seaborn tensorflow scikit-learn imbalanced-learn ucimlrepo

Usage

  1. Clone the Repository:

    git clone https://github.com/J3lly-Been/diabetic-retinopathy-classification.git
cd diabetic-retinopathy-classification

  2. Prepare the Data and Train the Model:

    Open the Jupyter notebook diabetic-retinopathy-classification.ipynb and follow the instructions within the notebook to load, clean, preprocess the dataset, build, train, and evaluate the neural network model.

Results

  • Test Accuracy: 78.9%
  • Test Loss: 0.51

License

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

Acknowledgments