Deep learning models for brain age prediction from magnetic resonance images

Abstract

This thesis focuses on developing and evaluating deep learning models for brain age prediction using magnetic resonance imaging (MRI) data. Building on the work of the Barcelonaβeta Brain Research Center (BBRC), the research aims to improve early diagnosis and intervention through accurate brain age prediction models. Advanced deep learning techniques are implemented to achieve lower error margins, validate model robustness across diverse datasets, and enhance interpretability. The ultimate goal is to contribute to the understanding and management of brain aging and neurodegenerative conditions. The results demonstrate significant advancements in predictive accuracy and model transparency.