  Convolutional Neural Networks (CNNs) have successfully been used to classify
diabetic retinopathy (DR) fundus images in recent times. However, deeper
representations in CNNs may capture higher-level semantics at the expense of
spatial resolution. To make predictions usable for ophthalmologists, we use a
post-attention technique called Gradient-weighted Class Activation Mapping
(Grad-CAM) on the penultimate layer of deep learning models to produce coarse
localisation maps on DR fundus images. This is to help identify discriminative
regions in the images, consequently providing evidence for ophthalmologists to
make a diagnosis and potentially save lives by early diagnosis. Specifically,
this study uses pre-trained weights from four state-of-the-art deep learning
models to produce and compare localisation maps of DR fundus images. The models
used include VGG16, ResNet50, InceptionV3, and InceptionResNetV2. We find that
InceptionV3 achieves the best performance with a test classification accuracy
of 96.07%, and localise lesions better and faster than the other models.
