Merge pull request #1 from akamathmw/master

Fixing path issue (to also run on MATLAB Online) and some minor typos

Author: akamathmw

.gitignore

@@ -1,7 +1,7 @@
-*.mat
-
-*.prj
-resources/
-
-ds000228-1.1.0/
-2DImageSet*
+*.mat
+
+*.prj
+resources/
+
+ds000228-1.1.0/
+2DImageSet*

BrainMRIAgeClassificationUsingDeepLearning.mlx

@@ -1,7 +1,7 @@
-Copyright (c) 2020, The MathWorks, Inc.
-All rights reserved.
-Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
-1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
-2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
-3. In all cases, the software is, and all modifications and derivatives of the software shall be, licensed to you solely for use in conjunction with MathWorks products and service offerings. 
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+Copyright (c) 2020, The MathWorks, Inc.
+All rights reserved.
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+3. In all cases, the software is, and all modifications and derivatives of the software shall be, licensed to you solely for use in conjunction with MathWorks products and service offerings. 
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

LICENSE.md

@@ -1,37 +1,37 @@
-# **Brain MRI Age Classification Using Deep Learning**
-
-This example shows how to work with an MRI brain image dataset and how to use transfer learning to modify and retrain ResNet-18, a pretrained convolutional neural network, to perform image classification on that dataset.
-
-The MRI scans used in this example were obtained during a study \[1\] of social brain development conducted by researchers at the Massachussets Institute of Technology (MIT), and are available for download via the OpenNEURO platform:
-    https://openneuro.org/datasets/ds000228/versions/1.1.0
-
-This example shows how horizontal midslice images from the brain MRI scan volumes can be classified into 3 categories according to the chronological age of the participant:
-1. Participants Aged 3-5
-2. Participants Aged 7-12
-3. Participants older than 18, classified as Adults
-
-![](images/overview.png)
-
-This example works though multiple steps of a deep learning workflow:
-- _Exploring_ a public brain MRI image dataset
-- _Preparing_ the dataset for deep learning
-- _Training_ a deep learning model to perform chronological age classification
-- _Evaluating_ the trained model
-
-
-## **Running the Example**
-
-Open and run the live script `BrainMRIAgeClassificationUsingDeepLearning.mlx`
-
-Requires:
-- [MATLAB](https://www.mathworks.com/products/matlab.html) (version R2019b or later)
-- [Deep Learning Toolbox](https://www.mathworks.com/products/deep-learning.html)
-- [Image Processing Toolbox](https://www.mathworks.com/products/image.html)
-
-## **References**
-\[1\] Richardson, H., Lisandrelli, G., Riobueno-Naylor, A., & Saxe, R. (2018). Development of the social brain from age three to twelve years. Nature Communications, 9(1), 1027. https://doi.org/10.1038/s41467-018-03399-2
-
-Copyright 2020 The MathWorks, Inc.
-
-[![View Brain-MRI-Age-Classification-using-Deep-Learning on File Exchange](https://www.mathworks.com/matlabcentral/images/matlab-file-exchange.svg)](https://www.mathworks.com/matlabcentral/fileexchange/74941-brain-mri-age-classification-using-deep-learning)
-
+# **Brain MRI Age Classification Using Deep Learning**
+
+This example shows how to work with an MRI brain image dataset and how to use transfer learning to modify and retrain ResNet-18, a pretrained convolutional neural network, to perform image classification on that dataset.
+
+The MRI scans used in this example were obtained during a study \[1\] of social brain development conducted by researchers at the Massachussets Institute of Technology (MIT), and are available for download via the OpenNEURO platform:
+    https://openneuro.org/datasets/ds000228/versions/1.1.0
+
+This example shows how horizontal midslice images from the brain MRI scan volumes can be classified into 3 categories according to the chronological age of the participant:
+1. Participants Aged 3-5
+2. Participants Aged 7-12
+3. Participants older than 18, classified as Adults
+
+![](images/overview.png)
+
+This example works though multiple steps of a deep learning workflow:
+- _Exploring_ a public brain MRI image dataset
+- _Preparing_ the dataset for deep learning
+- _Training_ a deep learning model to perform chronological age classification
+- _Evaluating_ the trained model
+
+
+## **Running the Example**
+
+Open and run the live script `BrainMRIAgeClassificationUsingDeepLearning.mlx`
+
+Requires:
+- [MATLAB](https://www.mathworks.com/products/matlab.html) (version R2019b or later)
+- [Deep Learning Toolbox](https://www.mathworks.com/products/deep-learning.html)
+- [Image Processing Toolbox](https://www.mathworks.com/products/image.html)
+
+## **References**
+\[1\] Richardson, H., Lisandrelli, G., Riobueno-Naylor, A., & Saxe, R. (2018). Development of the social brain from age three to twelve years. Nature Communications, 9(1), 1027. https://doi.org/10.1038/s41467-018-03399-2
+
+Copyright 2020 The MathWorks, Inc.
+
+[![View Brain-MRI-Age-Classification-using-Deep-Learning on File Exchange](https://www.mathworks.com/matlabcentral/images/matlab-file-exchange.svg)](https://www.mathworks.com/matlabcentral/fileexchange/74941-brain-mri-age-classification-using-deep-learning)
+