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sanjay-kv
SayantikaLaskar
Eye diseases such as glaucoma, diabetic retinopathy, and age-related macular degeneration can lead to severe vision impairment or blindness if not detected early. This project aims to develop a deep learning model to accurately classify various eye diseases from retinal images. The project involves several crucial tasks: sourcing and preprocessing a high-quality dataset of retinal images, performing exploratory data analysis to understand the dataset, and augmenting the data to improve model robustness. We will then select and implement deep learning architectures (such as Convolutional Neural Networks) to train and evaluate models, identify the best-performing model, and fine-tune it for optimal performance. The chosen model will be integrated into a deployable system capable of real-time image classification. Additionally, the model will be validated using a separate validation set or real-world clinical data to ensure its accuracy and reliability in a clinical setting. This issue aims to organize these tasks, assign responsibilities, and address any related questions to ensure the successful development and deployment of the eye disease classification system. Key considerations include the handling of imbalanced datasets, the selection of suitable deep learning algorithms, and ensuring the system's scalability and efficiency for clinical use.