The Inception-V3
37 architecture was used to address both the binary classification and the regression tasks. This architecture offers a very good tradeoff in terms of depth (313 layers) versus number of parameters (∼23 million). That is, it is very deep, as needed to better learn from images, while having a relatively small amount of parameters, which helps in preventing overfitting. The Inception-V3 models were trained by using a
transfer-learning cascade. Transfer learning
38 is a robust and efficient technique, in which training does not start from scratch, but rather from a “warmed-up” model (i.e., a model trained on another dataset to address a different question). In our study, the starting point was a model trained on the Kaggle Diabetic Retinopathy challenge
34 CFP dataset, whose training, in turn, started from a model trained on the ImageNet challenge
39 dataset of natural images. The transfer-learning cascade allows to build well-performing DL models when dealing with a relatively small dataset, such as that used in this study (
Fig. 1). More specifically, transfer learning involves replacing and training the softmax layer of the architecture in case of binary classification and the linear layer of the architecture in case of regression while keeping all the other layers frozen. This is followed by fine-tuning
38 of the weights throughout the network with the exception of a few initial layers close to the input. CFPs were resized to 299 × 299 pixels and normalized to [−1, 1], as required by the Inception-V3.
39 No additional preprocessing was performed on the images. The following parameters were used for training: 10 epochs for transfer-learning, 50 epochs for fine-tuning, and Adam optimizer with values of learning rate ranging in [10
−5, 10
−2].