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



Primary Advisor

Xiao-Hua (Helen) Yu, College of Engineering, Electrical Engineering Department


Alzheimer’s Disease (AD) is an irreversible, progressive brain disorder that impairs memory, thinking, and language. Known as the most common form of dementia, AD is the 6th leading cause of death in the United States. It is estimated that currently, nearly 6 million Americans suffer from AD and moreover, the prevalence of AD is projected to grow to 13.8 million being diagnosed by 2050. Considering these projections, hospitals are expected to be diagnosing nearly half a million patients a year. This high volume will lead to technological growth within the diagnosis process along with more effective treatments.

As of now, detecting AD in patients is very difficult due to the extremely subtle and slow forming cues that indicate AD. As such, many diagnoses of AD are done too late, resulting in the majority of treatments being ineffective. The current diagnosis of AD largely relies on documenting mental health decline, which typically results in late detection since AD has already caused severe brain damage. The most promising avenues for AD early detection rely on neuroimaging, more specifically Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, identifying a healthy brain from one with AD using MRI scans can still lead to inaccurate results. Thus, deep learning advances have been a popular solution for identifying these differences.

This amplifies the importance of early detection of which ADetect (our software product) will be able to accomplish. Earlier detection means more effective treatment. More effective treatment means more of those affected by AD will be able to lead more active and cognizant lifestyles. ADetect will utilize recent advances in deep learning to aid in the early detection process of AD. With MRI scans as the primary input for analysis, ADetect will be able to recognize the slow forming cues that often signal AD. Our product also allows hospitals and other customers to save more money as they are able to direct their physicians and radiologists to focus on more business-critical or higher priority activities.

Analysis of Senior Project Report.pdf (85 kB)
Analysis of Senior Project

Available for download on Monday, June 08, 2026