This example shows how to use transfer learning to modify and retrain ResNet-18, a pretrained convolutional neural network, to perform image classification on a brain MRI image dataset.
The MRI scans used in this example were obtained during a study  of social brain development conducted by researchers at the Massachusetts Institute of Technology (MIT). These data are available for download at the OpenNEURO platform  in NIfTI file format .
This example works with the 2D axial midslice images from the brain MRI scan volumes, and shows how these images 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
This example works though multiple steps of a deep learning workflow:
1. Exploring a public brain MRI image dataset
2. Preparing the dataset for deep learning
3. Training a deep learning model to perform chronological age classification
4. Evaluating the trained model
RUNNING THE EXAMPLE
Open and run the live script BrainMRIAgeClassificationUsingDeepLearning.mlx
 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://www.nature.com/articles/s41467-018-03399-2
 Cox, R. W., J. Ashburner, H. Breman, K. Fissell, C. Haselgrove, C. J. Holmes, J. L. Lancaster, D. E. Rex, S. M. Smith, J. B. Woodward, and S. C. Strother (2004). A (sort of) new image data format standard: NiFTI-1. 10th Annual Meeting of Organisation of Human Brain Mapping, Budapest, Hungary.
Vijay Iyer (2021). Brain-MRI-Age-Classification-using-Deep-Learning (https://github.com/matlab-deep-learning/Brain-MRI-Age-Classification-using-Deep-Learning/releases/tag/v1.1), GitHub. Retrieved .
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