Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network
Fig 5
The architecture of the deep convolutional neural network.
(a) Main layers and connections of the CNN that is employed in our study. It consists of five convolutional and overlapping max pooling layers, which are indicated by red rectangles and green rectangles respectively, followed by three fully-connected layers. (b), (d) Convolutional and overlapping max pooling operations are represented respectively. (c) The non-saturating activation function ReLU was represented. (ReLU: rectified linear units; CNN: convolutional neural network; SVM: support vector machine; ROI: region of interest; FC: full-connected operation; P1~P4: pixel value after pooling operation).