ResNet50-APTOS-DR

Diabetic Retinopathy Detection Model

Overview

This model is a deep learning-based classifier designed to detect and classify diabetic retinopathy (DR) from retinal fundus images. It is built on the ResNet50 architecture and trained on the APTOS 2019 Blindness Detection dataset, which includes five DR severity classes:

• 0: No DR
• 1: Mild DR
• 2: Moderate DR
• 3: Severe DR
• 4: Proliferative DR

The model aims to assist in early diagnosis and grading of diabetic retinopathy, reducing the workload for ophthalmologists and improving accessibility to screening.

Usage

You can use this model by cloning the repository and using the pickled model by <i>torch.load()</i>.

Dependencies Installation

Ensure you have the required dependencies installed:

pip install torch torchvision transformers opencv-python pandas

Loading the Model

Clone the repository (with GIT LFS enabled)

git lfs install

git clone https://huggingface.co/sakshamkr1/ResNet50-APTOS-DR

Load the Model

import torch
from PIL import Image

model = torch.load(model_path, map_location=torch.device('gpu'), weights_only=False) #Change torch.device to 'cpu' if using CPU
model.eval()

Transformer Application

from torchvision import transforms

transform = transforms.Compose([
    transforms.Resize((224, 224)),  # Resize image to match input size
    transforms.ToTensor(),  # Convert image to tensor
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # Normalize using ImageNet stats
])

Function to preprocess image and get predictions

import numpy as np

def predict(image_path):
    # Load and preprocess the input image
    image = Image.open(image_path).convert('RGB')  # Ensure RGB format
    input_tensor = transform(image).unsqueeze(0).to(device)  # Add batch dimension

    # Perform inference
    with torch.no_grad():
        outputs = model(input_tensor)  # Forward pass
        probabilities = torch.nn.functional.softmax(outputs, dim=1)  # Get class probabilities
    
    return probabilities.cpu().numpy()[0]  # Return probabilities as a NumPy array

# Test with an example image
image_path = "your_image_path"  # Replace with your test image path
class_probs = predict(image_path)

# Print results
print(f"Class probabilities: {class_probs}")
predicted_class = np.argmax(class_probs)  # Get the class with highest probability
print(f"Predicted class: {predicted_class}")

License

This model is released under the CC-BY-NC 4.0 license.