Project AMD is a deep learning initiative designed to assist in the early detection and classification of Age-Related Macular Degeneration (AMD). Using Convolutional Neural Networks (CNNs) and retinal fundus images, this system automates the diagnosis process to help medical professionals identify the disease stages (e.g., Normal, Dry AMD, Wet AMD).
🎯 Objectives To preprocess and augment medical retinal datasets.
To build and train a CNN model (e.g., ResNet, VGG19, or Custom CNN) for image classification.
To achieve high sensitivity and specificity in detecting macular degeneration.
To provide a user-friendly interface for uploading and testing images.
📂 Dataset This project utilizes retinal fundus images.
Source: [Insert Source, e.g., ODIR-5K, Kaggle AMD Dataset, or RFMiD]
Classes:
Normal
AMD (Early/Late/Wet/Dry)
Note: Due to privacy restrictions, the dataset is not included in this repository. Please download it from [Link] and place it in the data/ folder.
🛠️ Tech Stack Language: Python
Libraries:
TensorFlow / Keras or PyTorch (Deep Learning)
OpenCV (Image Processing)
NumPy & Pandas (Data Manipulation)
Matplotlib & Seaborn (Visualization)
Streamlit or Flask (Web Interface - if applicable)
🚀 Getting Started Follow these steps to set up the project locally.
Prerequisites Python 3.8 or higher
Git
Installation Clone the repository
Bash git clone https://github.com/abhijaypandey14/project-AMD.git cd project-AMD Create a Virtual Environment (Optional but recommended)
Bash python -m venv venv
venv\Scripts\activate
source venv/bin/activate Install Dependencies
Bash pip install -r requirements.txt 🧠 Usage
- Training the Model To retrain the model with your own dataset:
Bash python train.py --epochs 20 --batch_size 32 2. Testing/Inference To test the model on a single image:
Bash python predict.py --image "path/to/image.jpg" 3. Running the Web App (If applicable) If the project includes a UI:
Bash streamlit run app.py 📊 Results Accuracy: 9X.XX%
Loss: 0.XX
Confusion Matrix: (You can upload a screenshot of your confusion matrix or training graphs here)
🔮 Future Scope Integration with mobile applications for remote diagnosis.
Implementing Explainable AI (Grad-CAM) to visualize which parts of the retina the model is looking at.
Expanding the dataset to include other retinal diseases (Glaucoma, Diabetic Retinopathy).
🤝 Contributing Contributions are welcome!
Fork the Project
Create your Feature Branch (git checkout -b feature/AmazingFeature)
Commit your Changes (git commit -m 'Add some AmazingFeature')
Push to the Branch (git push origin feature/AmazingFeature)
Open a Pull Request
👤 Author Abhijay Pandey
GitHub: @abhijaypandey14
📄 License This project is licensed under the MIT License - see the LICENSE file for details.