DocumentCode
3532374
Title
Machine learning for very early Alzheimer´s Disease diagnosis; a 18F-FDG and PiB PET comparison
Author
Illan, I.A. ; Górriz, J.M. ; Ramírez, J. ; Chaves, R. ; Segovia, F. ; López, M. ; Salas-Gonzalez, D. ; Padilla, P. ; Puntonet, C.G.
Author_Institution
Dept. of Signal Theor., Networking & Commun., Univ. of Granada, Granada, Spain
fYear
2010
fDate
Oct. 30 2010-Nov. 6 2010
Firstpage
2334
Lastpage
2337
Abstract
This paper shows a machine learning approach based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) to compare the diagnostic accuracy on very early Alzheimer´s Disease (AD) patients with 18F FDG and Pittsburg Compound B (PiB) PET imaging. The Alzheimer´s Disease Neuroimaging Initiative (ADNI) dataset is used for testing, making use of the longitudinal character. Mild Cognitive Impairment (MCI) individuals that after a two years follow up converted into possible AD where used as very early AD patients. While 18F FDG and PiB have similar diagnostic accuracy in AD, PiB is shown to have higher discriminative power in very early AD with respect to FDG.
Keywords
diseases; learning (artificial intelligence); medical image processing; neurophysiology; positron emission tomography; principal component analysis; support vector machines; 18F-FDG PET; Alzheimer Disease Neuroimaging Initiative dataset; PCA; PiB PET; Pittsburg Compound B PET imaging; SVM; machine learning; mild cognitive impairment; principal component analysis; support vector machine; very early Alzheimer disease diagnosis; Accuracy; Alzheimer´s disease; Kernel; Positron emission tomography; Principal component analysis; Sensitivity; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Nuclear Science Symposium Conference Record (NSS/MIC), 2010 IEEE
Conference_Location
Knoxville, TN
ISSN
1095-7863
Print_ISBN
978-1-4244-9106-3
Type
conf
DOI
10.1109/NSSMIC.2010.5874201
Filename
5874201
Link To Document