Multi-Class Neural Networks | different features

This may be a wrong question or something so feel free to correct me :).

I have been studying neural networks for weeks now. I came across the multi-class classification model that uses neural networks.

As we see in this picture, the model allows you to classify your input into different classes. But this also assumes your input always use the same number features, right ? Meaning if I want to recognize handwritten digits, I should have images with the same dimension (for example 24x24) so I can use the same number of features (in this case 24x24=576). But what if one class, for example number 6, requires a different number of features (like the dimension of the handwritten digit 6 is 30x30 pixels)

• I know that the logical way to do this, is to have two different neural networks, but Is there any way to simultaneously train a multi-class classification model where inputs might use different features? What does research say about this?

About recent studies, I have not seen yet, but a typical solution can be employing PCA and using let's say its top-10 features as the input of a fully connected network, although for input patterns with a huge difference in the number of input dimensions I guess it is not logical.
• PCA stands for principal component analysis. As I've referred, CNNs' input also should be of the same size due to the connection of convolutional layers and dense layers. Oct 27 '18 at 11:02