Date of Award
2021
Document Type
Thesis
Degree Name
MS in Science
Department
Division of Computer Science, Mathematics and Science
First Advisor
Christoforou Christoforos
Abstract
Within the context of medical image diagnosis, we explore novel computational models to facilitate the detection of two medical conditions that burden our society. In particular, this research focuses on the use of deep learning models for the detection of Alzheimer’s Disease in Magnetic Resonance images (MRI) scans, as well as the detection of heart arrhythmias from electrocardiogram (ECG) recordings. We propose a novel architecture that depends on the 3D-CNN model to classify between MRI scans of cognitively healthy individuals and AD patients. Moreover, we explore the use of LSTM deep learning models to detect abnormal heart arrhythmias that present life-threatening challenges for individuals with underlying conditions that may not be recognized through current practices. The goal of this research is to measure the efficacy and predictability of applying deep learning techniques to detect AD by mapping the complex heterogeneity of the brain, and heart arrhythmias in ECG time-series recordings in a computational way.
Recommended Citation
Hogan, Ryan Christopher, "A COMPUTATIONAL APPROACH WITHIN MEDICAL RESEARCH" (2021). Theses and Dissertations. 342.
https://scholar.stjohns.edu/theses_dissertations/342