• 

  Classification

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  Interface

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  Result

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  Training

Eye Disease Detection using Deep Learning

A deep learning project to detect and classify eye diseases from images using computer vision.

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Project Description

This project leverages a convolutional neural network to classify eye diseases from retinal or eye image datasets.
The model aims to assist medical diagnostics by providing fast, automated, and accurate predictions of common eye conditions.

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Author

Ali Akbar Khan

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Technologies Used

• Python
  
• Fastai
  
• PyTorch
  
• Jupyter Notebook
  
• Gradio (optional for deployment)
  

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Model Details

• Architecture: resnet18 pretrained CNN
  
• Training: Fine-tuned for 10 epochs using Fastai's vision_learner
  
• Metric: Achieved ~92% accuracy on validation set

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Dataset

A labeled dataset of eye images was used, possibly sourced from Kaggle or medical image repositories.
The images represent different categories of eye diseases, such as:

• Cataract
  
• Glaucoma
  
• Retina disease
  
• Normal

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How to Run

1. Clone the repository

git clone https://github.com/aliiakbarkhan/eye-disease-detection-DL.git
cd eye-disease-detection-DL

2. Install dependencies

pip install -r requirements.txt

3. Run the notebook

Use Jupyter or VSCode to open waste-segregation.ipynb and run all cells.

4. Or launch Gradio app (if available)

python eye_app.py

Sample Predictions

The model can take any image of retina and classify it into one of the predefined categories with high accuracy. Here's a snapshot of predictions from the Gradio interface.

File Structure

waste-segregation/
├── eye_disease.ipynb     # Main notebook
├── eye_disease_model.pkl                   # Trained model (optional)
├── eye_app.py                      # Gradio app (if created)
├── README.md                   # Project documentation

Built With

• jupyter-notebook
• python