Classification of Alzheimer’s Disease from 18F-FDG and 11C-PiB PET Imaging Biomarkers Using Support Vector Machine

• Published in: Journal of medical and biological engineering Vol. 40; no. 4; pp. 545 - 554
• Main Authors: Yang, Bang-Hung, Chen, Jyh-Cheng, Chou, Wen-Hsiang, Huang, Wen-Sheng, Fuh, Jong-Ling, Liu, Ren‑Shyan, Wu, Cheng-Han
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2020; Springer Nature B.V
• Subjects: Algorithms; Alzheimer's disease; Biomarkers; Biomedical Engineering and Bioengineering; Cell Biology; Classification; Cognitive ability; Cortex (temporal); Dementia; Dementia disorders; Diagnosis; Engineering; Feasibility; Fluorine isotopes; Imaging; Learning algorithms; Machine learning; Magnetic resonance imaging; Medical imaging; Neurodegenerative diseases; Neuroimaging; Original Article; Positron emission; Positron emission tomography; Radiology; Support vector machines; Tomography; FDG; PiB; Biomarker; Support vector machine; Alzheimer's disease; Machine learning

Purpose Alzheimer’s disease (AD) is the most common type of dementia, and its early diagnosis has become a crucial issue. Machine learning provides a systematic and objective approach in classification. Currently, there are many studies using several kinds of neuroimaging modalities to perform class...