Tile-based Deep Learning for Brain Age Estimation

Using MRI scans of the brain, scientists can now predict brain health and further progress of diseases like Alzheimer’s. Still, traditional methods are slow and usually not as accurate, because MRI data is very complex and has a large volume. As a solution for these issues, this study introduces a new deep learning system that divides MRI scans into smaller and close-by patches. This strategy makes it easier to pick up minor regional details and still decrease the complexity of the model as well as the time required for training. CNNs that are specialized to identify small patterns in the brain help the system achieve better and more reliable brain age predictions. In addition, SHAP provides a way to understand and explain the important areas of the brain that influence the prediction of age. The study found that the offered approach performs better than any competing approach, both in making accurate predictions and saving on computing time. Thanks to its accuracy and ability to grow, this framework is suitable for assessing and monitoring any decline in cognitive functions as a person ages.