This chapter explains the use of Deep Learning Models from Artificial Intelligence (AI) that take Structural and Functional Magnetic Resonance Imaging (S/FMRI) data to classify Alzheimer's disease (AD) progression stages. Early and accurate diagnosis of AD is necessary for timely intervention, treatment planning, and providing personalized healthcare. Current limitations in diagnostic methods necessitate using AI methods such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to extract features from MRI data and develop models for predicting Mild Cognitive Impairment (MCI), AD, and Dementia. Initial results from a case study that applied the methodology demonstrated improved classification accuracy over traditional methods in accurately classifying disease stages and developing patient care. With more refinement as AI technologies progress, these computational approaches can drastically and positively change patient care. Healthcare professionals benefit from this chapter by understanding how AI can be implemented to deal with neurodegenerative diseases.
Authors: I F T I Khar Ali, Vijaya Kittu Manda
DOI: https://doi.org/10.4018/979-8-3693-7858-8.ch006
Publish Year: 2025
