… The dimension of the concatenated features is reduced by the first fully-connected layer. The
exact … It is reduced to 512 by the first fully-connected layer. The second fully-connected layer …

… a global average pooling layer followed by a dense layer for classification. Figure 9 shows
the Grad-CAM visualization of features classified using the proposed deep-ocular system. …

… In order to make our VGG-16 model train and predict on eight different ocular disease
classes, we had to append two Dense layers to the existing VGG-16 architecture. The overall …

… To recognize ocular surface disease on images, we propose new bilateral densely … double
outputs of each layer into the feature maps as inputs of the next layer, leading to a significant …

… propose a Dense Correlation Network (DCNet) for the patient-level ocular disease classification
task … While the final dense layers still distinguish between the diseases, this streamlined …

… ocular disease from the entered eye images. Generally, image processing is one of the most
efficient tools in AI to detect diseases, … The number of dense layers in VGG-16 is three, and …

… ocular diseases. The dataset used in this study was highly imbalanced and, with such a
dataset, classifying any disease … Each neuron in a dense layer is connected to all the neurons in …

… of common and prevalence eye diseases and complex to be … of ocular surface disorders
in accordance with densely … While our dense connection can save each layer information …

… Although we adopt ResNets truncating the fully connected layers as our backbone CNN [10],
it can be any classification network backbones. SCM captures spatial-level correlations …

… Ocular diseases affect different layers of OCT image … retina, is observed through the outer
plexiform layer (OPL). The … a dense correlation network (DCNet) to exploit the dense spatial …