<p>It looks like everything you want is available with the <a href="https://dltk.github.io/" rel="nofollow noreferrer">Deep Learning Toolkit (DLTK) for Medical Imaging</a></p>

<p>There is also a blog: <a href="https://blog.tensorflow.org/2018/07/an-introduction-to-biomedical-image-analysis-tensorflow-dltk.html" rel="nofollow noreferrer">An Introduction to Biomedical Image Analysis with TensorFlow and DLTK</a></p>

<p>There is a DataCamp course that walks you through most of the process but instead of a classifier they use deep learning to reconstruct brain images.  They provide a link to their MNIST classifier example which should be easy to adapt for your purpose.  See: <a href="https://www.datacamp.com/community/tutorials/reconstructing-brain-images-deep-learning" rel="nofollow noreferrer">Reconstructing Brain MRI Images Using Deep Learning (Convolutional Autoencoder)</a></p>

<p>ResearchGate has a thread that may help:
<a href="https://www.researchgate.net/post/What_is_the_appropriate_way_to_use_Nifti_files_in_deep_learning" rel="nofollow noreferrer">What is the appropriate way to use Nifti files in deep learning?</a></p>

CC BY-SA 4.0
2020-05-14T11:44:54.723Z
2020-05-14T12:18:19.260Z
2020-05-14T12:18:19.260Z
Brian O'Donnell
<p>I have a dataset with <strong>MRI</strong> of patients with a specific disease that affects the <strong>brain</strong> and another dataset with <strong>MRI</strong> of healthy patients.</p>

<p>I want to create a <strong>classifier</strong> (using neural networks) to classify if the MRI of a new patient shows the presence of the illness or not.</p>

<p>First of all, I <strong>extracted</strong> the brain from all the MRIs (the so-called <strong>skull stripping</strong>) using <a href="https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET" rel="nofollow noreferrer">BET</a> tool found in FSL.</p>

<p>I have three questions for you</p>

<ol>
<li><p>As the input to the <strong>training</strong> phase, I want to give the <strong>whole</strong> extracted brains (possibly in the nii format). What kind of <strong>preprocessing</strong> steps do I need to apply once I've <strong>extracted</strong> the brains (before passing it to the classifier)?</p></li>
<li><p>Do you know any better tool for <strong>skull stripping</strong>?</p></li>
<li><p>Do you know a tool (or library) that takes as input a nii files and allows me to create a classifier that uses neural networks?</p></li>
</ol>

CC BY-SA 4.0
2020-05-14T10:29:23.357Z
Please, focus on one question only. I would remove at least the second question and maybe leave the 3rd question as a side note.
nbro
<neural-networks><machine-learning><data-preprocessing><brain>
Are my steps correct for a proper classification of a sick brain?
AleWolf
# Are my steps correct for a proper classification of a sick brain?

(Asked by: username_0 on 2020-05-14)

I have a dataset with **MRI** of patients with a specific disease that affects the **brain** and another dataset with **MRI** of healthy patients.

I want to create a **classifier** (using neural networks) to classify if the MRI of a new patient shows the presence of the illness or not.

First of all, I **extracted** the brain from all the MRIs (the so-called **skull stripping**) using [BET](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET) tool found in FSL.

I have three questions for you

1. As the input to the **training** phase, I want to give the **whole** extracted brains (possibly in the nii format). What kind of **preprocessing** steps do I need to apply once I've **extracted** the brains (before passing it to the classifier)?
2. Do you know any better tool for **skull stripping**?
3. Do you know a tool (or library) that takes as input a nii files and allows me to create a classifier that uses neural networks?

**Tags:** neural-networks, machine-learning, data-preprocessing, brain

> **Comments:**
> **username_1**: Please, focus on one question only. I would remove at least the second question and maybe leave the 3rd question as a side note.  

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## 1 answers

### Answer by username_2 on 2020-05-14. Score: 2

It looks like everything you want is available with the [Deep Learning Toolkit (DLTK) for Medical Imaging](https://dltk.github.io/)

There is also a blog: [An Introduction to Biomedical Image Analysis with TensorFlow and DLTK](https://blog.tensorflow.org/2018/07/an-introduction-to-biomedical-image-analysis-tensorflow-dltk.html)

There is a DataCamp course that walks you through most of the process but instead of a classifier they use deep learning to reconstruct brain images. They provide a link to their MNIST classifier example which should be easy to adapt for your purpose. See: [Reconstructing Brain MRI Images Using Deep Learning (Convolutional Autoencoder)](https://www.datacamp.com/community/tutorials/reconstructing-brain-images-deep-learning)

ResearchGate has a thread that may help:
[What is the appropriate way to use Nifti files in deep learning?](https://www.researchgate.net/post/What_is_the_appropriate_way_to_use_Nifti_files_in_deep_learning)


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