I am building model with medical dataset using deep learning methods.

Medical dataset consists of both numerical data such as age, sex and images of xray scans(1024 x 1024) .

Labels consists of types of cancer .

I believe that ages and sex gonna affect output of network.

But including images will make the network biased towards images, because images will occupy most of the input layer.

how can I design the input layer of network ?

additional information: I am not using CNN but normal deep learning network with two hidden layers

  • $\begingroup$ Very confusing! A neural network with two hidden layers is not a deep learning network. $\endgroup$ – Brian O'Donnell Dec 20 '17 at 3:31
  • $\begingroup$ @BrianO'Donnell According to whom? To you? $\endgroup$ – nbro Dec 21 '17 at 22:30

I would give some advice here:

You should probably use Convolutional Neural Network for image data or you'll end up with too many input features, and too much computation. What are your constraints on this point?

I would not mix image data with numerical data. Maybe you can implement two neural networks, one handling image data and another managing the numerical data, each one outputting their results. A second step would be to use their results to handcraft a global result or let another NN manage this.


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