Develop a simple image classification system by using Laplacian of Gaussian (LoG)
The two simplest ideas are probably to either pad the smaller images with a constant pixel value to a common shape, or crop the bigger images to a common shape.

In the first case, you will however likely end up with a network biased towards padded images/samples with constant pixel value areas. In the second case, you loose possibly prominent information with respect to your classification task.

You could also use the cropping approach with several crops per image in order to avoid loss of information in your data set, leading to an increased proportion of the classes associated with bigger images in your training set. This also biases your neural network (this could be more or loss controlled thanks to appropriate weights attached to the classes' samples during training, if you can predict the imbalance before training, cf. this towardsdatascience article to read about training with imbalanced classes, with Keras code).

Those questions were raised and discussed in this SO post.