This project aims to identify and classify eye diseases from provided eye images, focusing on conditions such as diabetic retinopathy, cataract, and glaucoma. By leveraging deep learning techniques, the project utilizes Convolutional Neural Networks (CNNs) to detect these diseases with high accuracy.
- Deep Learning Framework: Built using TensorFlow and Keras libraries.
- Image Processing: Eye images are preprocessed, including resizing, rescaling, and data augmentation, to improve model performance.
- CNN Model: A robust CNN architecture has been trained to classify eye diseases.
- Confidence Scores: For each prediction, the model provides a confidence score, indicating the certainty of its classification.
- Dataset: The model is trained and validated using a dataset containing labeled images of different eye conditions.
- Training Strategy: The data is split into training (80%), validation (10%), and testing (10%) sets to ensure effective model learning and evaluation.
- 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.
This project has significant implications for early detection of eye diseases, aiding in timely diagnosis and treatment, especially in areas where medical resources are limited.
