This project focuses on using deep learning techniques for the detection of diabetic retinopathy. Diabetic retinopathy is a diabetes-related eye condition that can lead to vision loss if not detected early. Leveraging the power of deep learning, specifically the ResNet-50 architecture, we aim to automatically identify signs of diabetic retinopathy in retinal images.
We have employed the ResNet-50 architecture, a deep convolutional neural network known for its ability to effectively learn hierarchical features from images. ResNet-50's use of residual blocks allows for the training of very deep networks, making it suitable for complex image recognition tasks.