Applying deep learning on large images is always a challenge but there a solution using convolutional but first, let's understand in brief where is the challenge? So one of the challenges in computer vision is that inputs become very big as we increase image size. Consider the example of a basic image classification problem e.g.; cat detection. Let's take an input 64 64 image of a cat and try to figure out if that is a cat or not? to do that we'll need 64x64x3, (where 3 is the no of RGB channel) parameters, so the x input feature will have 12288 dimensions though this is not a very large number considering just the 64 64 image size which is very small there are lots of input features to deal with.. This is just for a 1MB image but in computer vision problem you don't want to stick with using just tiny images using bigger images results in overfitting and huge input feature vector so here comes convolution operation which is the basic building block of Convolutional Neural Network (CNN).