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In absolute, it is one CNN wich takes 3 inputs images. You could see it as 3 separate features extractors (CNN) which merge their results while trained together.
The author obtain 3 2D input from a 3D images by keeping 3 2D images; one in each plane.
Each of these images has multiple channel because they slices the input among the respective axis.
It is ...
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It is possible to build that kind of a CNN. It is important to maintain uniform distribution for both the classes ('cat' and 'not cat'). That is you should have an almost equal number of samples for each of these classes to avoid biasing your model to the 'non-cat' class just because it has huge number of examples.
The number of non-cat examples can be ...
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