Answers.Body: <p>I would give some advice here:</p>
  
  <p>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?</p>
  
  <p>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.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-12-27T13:37:46.167Z
Answers.LastActivityDate: 2017-12-27T13:37:46.167Z
Answers.UserDisplayName: Glassfrog
Body: <p>I am building a feed-forward neural network with two hidden layers, which I will train with a medical dataset, which consists of both data, such as age and sex, and images of x-ray scans (<span class="math-container">$1024 \times 1024$</span>). The labels are types of cancer. </p>
  
  <p>I believe that ages and sex will affect the output of the network. But including the images will make the network biased towards the images, because images will occupy most of the input layer.  </p>
  
  <p>How can I design the input layer of the network? </p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-11-29T07:05:08.983Z
Comments.Text: @BrianO'Donnell A neural network with two hidden layer is a deep neural network
Comments.UserDisplayName: kkr4k
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-11-29T19:45:48.010Z
Comments.Text: @kkr4k No expert in neural networks considers two hidden layers as a 'deep neural network.  nbro corrected the original erroneous reference to 'deep neural network'.
Comments.UserDisplayName: Brian O'Donnell
Tags: <deep-learning><deep-neural-networks>
Title: How can I design the input layer of a feed-forward neural network to be trained with a medical dataset with three features?
UserDisplayName: santhosh kumar
ThreadText: # How can I design the input layer of a feed-forward neural network to be trained with a medical dataset with three features?
  
  (Asked by: username_0 on 2017-12-16)
  
  I am building a feed-forward neural network with two hidden layers, which I will train with a medical dataset, which consists of both data, such as age and sex, and images of x-ray scans ($1024 \times 1024$). The labels are types of cancer.
  
  I believe that ages and sex will affect the output of the network. But including the images will make the network biased towards the images, because images will occupy most of the input layer.
  
  How can I design the input layer of the network?
  
  **Tags:** deep-learning, deep-neural-networks
  > **Comments:**
  > **username_1**: @BrianO'Donnell A neural network with two hidden layer is a deep neural network  
  > **username_2**: @kkr4k No expert in neural networks considers two hidden layers as a 'deep neural network.  nbro corrected the original erroneous reference to 'deep neural network'.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2017-12-27. Score: 1
  
  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.
  
  
  ---
  
  