Deep Learning for Early Alzheimer Disease Detection with MRI Scans
Journal:
arXiv
Published Date:
Jan 17, 2025
Abstract
Alzheimer's Disease is a neurodegenerative condition characterized by
dementia and impairment in neurological function. The study primarily focuses
on the individuals above age 40, affecting their memory, behavior, and
cognitive processes of the brain. Alzheimer's disease requires diagnosis by a
detailed assessment of MRI scans and neuropsychological tests of the patients.
This project compares existing deep learning models in the pursuit of enhancing
the accuracy and efficiency of AD diagnosis, specifically focusing on the
Convolutional Neural Network, Bayesian Convolutional Neural Network, and the
U-net model with the Open Access Series of Imaging Studies brain MRI dataset.
Besides, to ensure robustness and reliability in the model evaluations, we
address the challenge of imbalance in data. We then perform rigorous evaluation
to determine strengths and weaknesses for each model by considering
sensitivity, specificity, and computational efficiency. This comparative
analysis would shed light on the future role of AI in revolutionizing AD
diagnostics but also paved ways for future innovation in medical imaging and
the management of neurodegenerative diseases.
