Answers.Body: <p>CNN is used since it is effectively an optimized use case for dealing with image data. </p>
  
  <p>CNN effectively automatically extracts features from images. Other techniques are more likely to not take full advantage of the data. CNN is able to make full use of the data by also including information from adjacent pixels and downsample through layers.  </p>
  
  <ul>
  <li><a href="https://www.sciencedirect.com/science/article/pii/S1877050918309335" rel="nofollow noreferrer">Here</a> is a paper on the performance of CNN on image data</li>
  <li><a href="https://arxiv.org/pdf/1910.06548.pdf" rel="nofollow noreferrer">Here</a> is a paper comparing CNN's to other methods</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-01-16T17:19:02.460Z
Answers.LastActivityDate: 2020-06-13T00:23:40.503Z
Answers.LastEditDate: 2020-06-13T00:23:40.503Z
Answers.UserDisplayName: Ryan Marinelli
Body: <p>I'm a newbie in artificial intelligence.</p>
  
  <p>I have started to research how to do image segmentation and all the papers that I have found are about CNN. Most of them use the same network, U-net, but with little variations: with more or fewer layers, different parameter values, etc.; but with not very different results.</p>
  
  <p>It seems that CNNs are in fashion and everyone uses them. Or there are other reasons that I don't know.</p>
  
  <p>If everyone is getting not very different results, why are they using the same approach instead of trying different ones?</p>
  
Tags: <convolutional-neural-networks><applications><image-segmentation><u-net>
Title: Why everyone is using CNN for image segmentation?
UserDisplayName: VansFannel
ThreadText: # Why everyone is using CNN for image segmentation?
  
  (Asked by: username_0 on 2020-01-16)
  
  I'm a newbie in artificial intelligence.
  
  I have started to research how to do image segmentation and all the papers that I have found are about CNN. Most of them use the same network, U-net, but with little variations: with more or fewer layers, different parameter values, etc.; but with not very different results.
  
  It seems that CNNs are in fashion and everyone uses them. Or there are other reasons that I don't know.
  
  If everyone is getting not very different results, why are they using the same approach instead of trying different ones?
  
  **Tags:** convolutional-neural-networks, applications, image-segmentation, u-net
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-01-16. Score: 2 | Accepted answer
  
  CNN is used since it is effectively an optimized use case for dealing with image data.
  
  CNN effectively automatically extracts features from images. Other techniques are more likely to not take full advantage of the data. CNN is able to make full use of the data by also including information from adjacent pixels and downsample through layers.
  
  * [Here](https://www.sciencedirect.com/science/article/pii/S1877050918309335) is a paper on the performance of CNN on image data
  * [Here](https://arxiv.org/pdf/1910.06548.pdf) is a paper comparing CNN's to other methods
  
  
  ---
  
  