There are four main layers that form a convolutional neural network:
Convolution: These are layers consisting of entities called filters that are used as parameters to train the network.
ReLu: It is used as the activation function and is always used with the convolution layer.
Pooling: Pooling is the concept of shrinking the complex data entities that form after convolution and is primarily used to maintain the size of an image after shrinkage.
Connectedness: This is used to ensure that all of the layers in the neural network are fully connected and activation can be computed using the bias easily.