# Feature Eye Disease Classified RNN

[praveenarjun]

Description

This feature aims to implement a Recurrent Neural Network (RNN) model to classify eye diseases from medical images. The model will be trained to identify various eye conditions such as diabetic retinopathy, glaucoma, and macular degeneration based on retinal images. Goals

To provide an automated system for early detection and diagnosis of eye diseases.
To enhance the learning experience for users by introducing advanced neural network techniques.
To contribute to the repository's collection of real-world machine learning applications.

Technical Details

Data Source: Use publicly available retinal image datasets such as the EyePACS dataset.
Model Architecture: Implement an RNN architecture suitable for image classification, potentially integrating Convolutional Neural Networks (CNNs) for feature extraction.
Tools and Libraries: TensorFlow or PyTorch for model development, OpenCV for image preprocessing.
Performance Metrics: Accuracy, Precision, Recall, and F1-Score.

Steps

Data Collection: Gather and preprocess the retinal image dataset.
Model Design: Design the RNN architecture, incorporating necessary preprocessing and feature extraction layers.
Training and Evaluation: Train the model on the dataset and evaluate its performance using the defined metrics.
Deployment: Integrate the trained model into a user-friendly interface for real-time classification.

Contribution Guidelines

Follow the existing standards and practices outlined in the repository's README and documentation.
Ensure that the code is modular, well-documented, and includes unit tests.

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