This is the traditional Deep CNN network
Here instead of flattening or feature extraction by convolution, RELU and pooling layers, we use
our specialized GLCM+LBP combined feature vector to represent the image for further
classification
The Fully Connected layer is a traditional Multi Layer Perceptron that uses a softmax activation
function in the output layer. The term “Fully Connected” implies that every neuron in the
previous layer is connected to every neuron on the next layer. The output from feature extraction
stage represent high-level features of the input image. The purpose of the Fully Connected layer
is to use these features for classifying the input image into various classes based on the training
dataset.
