🩺 Diabetic Retinopathy Detection

A deep learning project to detect the severity of Diabetic Retinopathy using retinal fundus images.
The model classifies images into 5 stages:

• No DR
• Mild
• Moderate
• Severe
• Proliferative DR

Built using Transfer Learning with ResNet18.

📁 Project Structure

Diabetic-Retinopathy-Detection/
│── train.csv
│ 
│
│── train.ipynb
│ 
│
│── models
|      └──retino_model.h5(Generated after training)
│
│── templates/
│ └── index.html
│
│── main.py
│
│
│── .gitignore
│── requirements.txt
│── README.md

🔍 Dataset

• Source: Kaggle Dataset
• Directory: colored_images/ with subfolders for each DR category.
• Labels: Provided in train.csv

🧠 Model Details

• Framework: TensorFlow / Keras
• Architecture: ResNet18 via transfer learning
• Classification Type: Multiclass (5 classes)
• Final Model Output: retino_model.h5
• Achieved Accuracy: 69%

How to Run:

Step 1: Install Dependencies

pip install -r requirements.txt

Step 2: Train the model

jupyter notebook notebooks/train.ipynb

Step 3: Run the fastapi webapp

python src/main.py

✅ Features

• Classifies 5 stages of diabetic retinopathy
• Uses Transfer Learning with ResNet18
• Flask web interface for predictions
• Based on a real-world medical dataset