The categorical classification is performed by using all three of the neural network architectures: VGG16, ResNet50, and InceptionV3. The best results were obtained using the InceptionV3 version of the transfer learning network for this diabetic retinopathy use case. In case of categorical classification we are just converting the class with the maximum predicted class probability as the predicted severity label. However since the classes in the problem has an ordinal sense one of the ways in which we can utilize the softmax probabilities is to take the expectation of the class severity with respect to the softmax probabilities and come up with an expected score 
as follows:
We can rank order the scores and determine three thresholds to determine which class the image belongs to. These thresholds can be chosen by training a secondary...
