Abstract: Brain age estimation is an emerging field in medical imaging, particularly useful for detecting neurological diseases and age-related cognitive decline. This project aims to develop a robust model for predicting brain age using T1-weighted MRI scans. By analyzing the structural patterns within these scans, the model will estimate the biological age of a patient’s brain. The deviation between the predicted brain age and the chrono- logical age may indicate the presence of neurological diseases such as Alzheimer’s, Parkinson’s, or other neurodegenerative conditions. The project will leverage deep learning algorithms to process MRI data and predict brain age accurately. Various preprocessing steps will be applied to ensure high-quality input for the model, and advanced neural network architectures will be utilized for prediction. The ultimate goal is to provide a tool that aids in early diagnosis of neurological conditions by identifying patients whose brains show signs of accelerated aging. This system, if effective, can enhance early detection and interven- tion strategies, improving patient outcomes and contributing to personalized healthcare solutions.
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DOI:
10.17148/IJARCCE.2025.14440
