# What might a correct approach be, to image preprocessing for CNN for these specialised images

I am trying to understand the the best practices for preprocessing images prior to neural network training and I have some uncertainties despite reading extensively online about it. I am also uncertain if what I am reading (which is about color natural images with an RGB channel applies to my images, which are greyscale medical images like this one:

First, with an image such as this the R, B and G values in the RGB channel are identical. Many of the CNNs I have been experimenting on were developed on natural color images (such as AlexNet). Does this impact how such CNN might handle and greyscale image? If the input into the network is meant to be (3, 256, 256) then for an image like this, should that be changed to (1, 256, 256) or do the identical R,B and G values not matter?

Also, regarding normalisation. If I was to normalise the images with the mean pixel value and standard deviation, are those values the mean pixel value across the whole dataset or just that one image?

Does image size make a difference? For a human, a higher resolution image may be easier to interpret but does this also apply to a neural network?

I apologise for these simple questions. There are "half" answers to many of these questions (and I have followed many tutorials including Andrew Ngs) but my problem (these unusual images) is quite specialised and I have a feeling my image pre processing is incorrect. I also know if I get this wrong, I could waste weeks on data thats incorrectly prepared so some expert opinion would be great.

1) The input should be $(1,256,256)$. You should read about convolutional neural nets to understand better how images are processed. Your initial convolutional layer filters will have dimensions $(1,H,W)$, as there is no need to consider the color-depth of the image since you have one channel.