This project aims to develop an AI-based solution for classifying eye diseases from retinal images. Using a manually collected dataset 📸, the model identifies four types of eye diseases with high accuracy. The project leverages state-of-the-art deep learning models, including ResNet50, to ensure reliable performance.
1️⃣ Data Collection: images were manually gathered to build a robust dataset.
2️⃣ Data Preprocessing: Cleaning and resizing images to ensure consistency.
3️⃣ Model Development: Training multiple models using the fastai library to achieve the highest accuracy.
4️⃣ Deployment: Integrating the trained model into a mobile application using Flutter, with backend support through Gradio or Hugging Face for interactive predictions.
✅ Recognized as one of the top university projects 🎖️
🔗 Official Project Recognition
✅ Achieved an impressive 91% accuracy in classifying eye diseases 🏅
✅ Successfully deployed the model on platforms like Hugging Face and integrated it with a Flutter-based mobile application 📱
🔹 Expanding the dataset for improved accuracy and generalization.
🔹 Implementing a user-friendly interface for seamless interaction.
🔹 Exploring real-time image processing for instant predictions.
🔗 This project aims to assist healthcare professionals in diagnosing eye diseases more efficiently, reducing manual errors, and enhancing patient care. 🏥👁️
You can try the model here:- https://huggingface.co/spaces/Mez01/mezo
mosayedms123@gmail.com LinkedIn: www.linkedin.com/in/m0hamed-sayed