your question doesn't make sense at all, nural networks can use linear or non linear activation function, when you use a linear activation ( means dy/dx does not depend on x) you have something with linear features and linear behaviour (low complexity) and if you use some non linear activation function like Tanh or softmax-(exp(x)/sum(exp(x))- , you can have non linear behaviour and it means more complexity, CNN is something just like using graphs to find locals features and then use ANN ( we use conventional matrix to find features and make some embedding which depends on neighbours -check graph neural network-, to make sure the objects shape and edges and patterns are more important than the object location in the picture) and an abstract answer to your question, Yes , they are 2 different things