Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Robert Chun
Second Advisor
Wendy Lee
Third Advisor
Thomas Austin
Keywords
ADHD, neuroimaging, machine learning, feature extraction, MRI
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopment disorder that can significantly affect a person’s attention, impulse control, and executive function. Currently, the traditional diagnosis method often relies on clinical assessments and observations. However, these methods can be subjective and lead to inconsistencies in diagnosis between individuals. To address this challenge, neuroimaging and machine learning (ML) are promising tools for providing a more objective diagnosis of ADHD. The goal of this project is to apply a multimodal approach in which structural and functional features of specific regions of the brain are used to develop a more accurate and objective diagnostic tool for ADHD using a deep learning framework. The results of this research indicate that there are some potential but also limitations to combining the modalities together as compared to using one modality alone.
Recommended Citation
Pham, Isabel, "Multimodal Feature Fusion and Machine Learning for ADHD Detection Using Neuroimaging Data" (2025). Master's Projects. 1467.
https://scholarworks.sjsu.edu/etd_projects/1467
