College - Author 1
College of Engineering
Department - Author 1
Electrical Engineering Department
Degree Name - Author 1
BS in Electrical Engineering
College - Author 2
College of Engineering
Department - Author 2
Electrical Engineering Department
Degree - Author 2
BS in Electrical Engineering
College - Author 3
College of Engineering
Department - Author 3
Electrical Engineering Department
Degree - Author 3
BS in Electrical Engineering
Date
6-2023
Primary Advisor
Xiao-Hua Yu, College of Engineering, Electrical Engineering Department
Abstract/Summary
Alzheimer’s is the 7th leading cause of death in the United States, according to the National Institute of Health [1]. If left undiagnosed, Alzheimer’s leads to progressive cognitive decline and ultimately, dementia. Currently, in order to fully diagnose early-onset Alzheimer’s, multiple sessions of lengthy tests are needed, requiring excessive time and resources that some individuals may not have access to. In addition to minimizing the number of tests needed, this project will strive to improve both the accuracy and reliability rates of recognizing and identifying early signs of Alzheimer’s development from Magnetic Resonance Image (MRI) brain scans. Currently, MRI scans as a sole means of diagnosis are not accurate for early detection since about 33% of individuals receive an incorrect diagnosis [2].
To address this problem, this project uses a convolutional neural network (CNN) to determine when a person may be showing signs of early-onset Alzheimer’s disease. The project will use the ADNI1 (Alzheimer’s Disease Neuroimaging Initiative) dataset to train and test the neural network. The CNN will be used for feature extraction as it is trained to search for anomalies in MRI scan and determine whether the patient has Alzheimer’s disease (AD) or is cognitively normal (NL).
URL: https://digitalcommons.calpoly.edu/eesp/607