An input layer, an output layer, and multiple hidden layers make up convolutional networks. The neurons in the layers of a convolutional network are arranged in three dimensions, unlike those in a standard neural network (width, height, and depth dimensions). This enables the CNN to convert a three-dimensional input volume into an output volume. Convolution, pooling, normalizing, and fully connected layers make up the hidden layers. Multiple Cconv layers are used in CNN to filter input volumes to higher levels of abstraction.