From many definitions that I read, I concluded that a DNN (deep neural network) is an ANN (artificial neural network) that have more than one hidden layer.
Knowing that CNN (convolutional neural network, a kind of a DNN) includes a stage of feature extraction (through convolution operations then pooling), my question is:
Does any DNN naturally considers feature extraction? For example, if we assume a simple architecture that includes three fully-connected hidden layers. I think not. If it is really not, how a step of feature extraction can be introduced within such a DNN? Should I necessarily introduce convolution like in CNN?