Figure 2

Proposed approaches for building deep learning models. Consider a classification problem (class number = n + 1) of normal and disease cases with n subtypes. (a) Proposed approach 1: flat classification is used to directly classify normal eyes and those with four subtypes of disease with transfer learning from an ImageNet-pretrained CNN model to create Model 1. (b) Proposed approach 2: hierarchical classification is used to create a low-level model (Model 2) for classifying normal versus disease cases and a high-level model (Model 3) for classifying subtypes of disease. Both models apply transfer learning from the ImageNet-pretrained CNN model. The confidence in a ‘normal’ result from Model 2 and the confidence in disease subtypes from Model 3 are concatenated to calculate the overall result from training Metamodel 1. (c) Proposed approach 3: hierarchical classification using hierarchy transfer learning between different-level models is used in the hierarchical classification model is. In contrast with proposed approach 2, the high-level model (Model 4) for classifying disease subtypes, transfer learning is from the low-level model (Model 2) instead of from the ImageNet-pretrained CNN model. The normal confidence from Model 2 and the disease subtype confidence from Model 4 are concatenated to train Metamodel 2 to calculate the overall result.