CNN is just a kind of neural network; its convolutional layer differs from other neural networks. To perform image classification, CNN goes through every corner, vector and dimension of the pixel matrix. Performing with this all features of a matrix makes CNN more sustainable to data of matrix form. Convolutional layers consist of multiple features like detecting edges, corners, and multiple textures, making it a special tool for CNN to perform modeling. That layer slides across the image matrix and can detect its all features. This means each convolutional layer in the network can detect more complex features. As the feature expands, we need to expand the dimension of the convolutional layer.