{"id":19476774,"url":"https://github.com/yukta026/eye_disease_prediction","last_synced_at":"2026-05-10T23:55:13.053Z","repository":{"id":256077511,"uuid":"854266477","full_name":"Yukta026/Eye_disease_prediction","owner":"Yukta026","description":"Detection and Classification of Eye Diseases: Diabetic Retinopathy, Cataract, and Glaucoma Using CNN","archived":false,"fork":false,"pushed_at":"2024-09-10T13:13:35.000Z","size":3390,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-25T16:27:08.636Z","etag":null,"topics":["classification-model","cnn-classification","cnn-keras","cnn-tensorflow","data-augmentation","machine-learning","prediction-model","python3"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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aims to **identify and classify eye diseases** from provided eye images, focusing on conditions such as **diabetic retinopathy, cataract, and glaucoma**. \nBy leveraging deep learning techniques, the project utilizes **Convolutional Neural Networks (CNNs)** to detect these diseases with high accuracy.\n\n## Key Features\n- **Deep Learning Framework**: Built using **TensorFlow** and **Keras** libraries.\n- **Image Processing**: Eye images are preprocessed, including resizing, rescaling, and data augmentation, to improve model performance.\n- **CNN Model**: A robust CNN architecture has been trained to classify eye diseases.\n- **Confidence Scores**: For each prediction, the model provides a confidence score, indicating the certainty of its classification.\n\n## Model Highlights\n- **Dataset**: The model is trained and validated using a dataset containing labeled images of different eye conditions.\n- **Training Strategy**: The data is split into training (80%), validation (10%), and testing (10%) sets to ensure effective model learning and evaluation.\n- **Real-time Prediction**: The trained model can be used to make predictions on new, unseen images, outputting both the predicted disease and the confidence level of that prediction.\n\n## Applications\nThis project has significant implications for early detection of eye diseases, aiding in timely diagnosis and treatment, especially in areas where medical resources are limited.\n\n## Demonstration - \n\u003cimg width=\"408\" alt=\"Screenshot 2024-09-08 at 5 22 10 PM\" src=\"https://github.com/user-attachments/assets/b4388f26-635f-40f9-abcd-9f5fc159d566\"\u003e\n\n## References - \n1) https://www.kaggle.com/datasets/gunavenkatdoddi/eye-diseases-classification\n2) https://www.youtube.com/watch?v=dGtDTjYs3xc\u0026list=PLeo1K3hjS3ut49PskOfLnE6WUoOp_2lsD 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