Figure 1.

Deep neural network structures. (A) Convolutional neural network (CNN) imaging flow: Fundus images are input and sequentially transformed by convolution, pooling, and fully connected layers, into flattened vectors. Output vector (Softmax layer) elements denote the probabilities for disease presence. In training, lower layers (left) learn features to influence the high-level representations (right), by which internal network layer parameters are iteratively adjusted to enhance accuracy. (B) General architectures of deep learning models in mainstream.