Compared to Densely Connected Neural Networks, the CNN uses lesser parameters and thus scales well over larger input data and is easier to deploy as applications.
The CNN's architecture gives its strength. There are basically two main activities: (1) feature extraction via convolutions, and (2)classification. Input data (Images) are represented as tensors of dimensions: H-Height of image in pixels, W-Width of image in pixels, Color channel dimensions - 1 for Gray color and 3 for RGB color.