Brain-MRI-Age-Classification-using-Deep-Learning

MATLAB example using deep learning to classify chronological age from brain MRI images

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

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 (version R2019b or later)
• Deep Learning Toolbox
• Image Processing Toolbox

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

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