  Diabetic retinopathy (DR) is a retinal microvascular condition that emerges
in diabetic patients. DR will continue to be a leading cause of blindness
worldwide, with a predicted 191.0 million globally diagnosed patients in 2030.
Microaneurysms, hemorrhages, exudates, and cotton wool spots are common signs
of DR. However, they can be small and hard for human eyes to detect. Early
detection of DR is crucial for effective clinical treatment. Existing methods
to classify images require much time for feature extraction and selection, and
are limited in their performance. Convolutional Neural Networks (CNNs), as an
emerging deep learning (DL) method, have proven their potential in image
classification tasks. In this paper, comprehensive experimental studies of
implementing state-of-the-art CNNs for the detection and classification of DR
are conducted in order to determine the top performing classifiers for the
task. Five CNN classifiers, namely Inception-V3, VGG19, VGG16, ResNet50, and
InceptionResNetV2, are evaluated through experiments. They categorize medical
images into five different classes based on DR severity. Data augmentation and
transfer learning techniques are applied since annotated medical images are
limited and imbalanced. Experimental results indicate that the ResNet50
classifier has top performance for binary classification and that the
InceptionResNetV2 classifier has top performance for multi-class DR
classification.
