RT Journal Article
T1 Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease.
A1 Ortiz-García, Andrés
A1 Munilla-Fajardo, Jorge
A1 Górriz-Sáez, Juan Manuel
A1 Ramírez, Javier
K1 Alzheimer, Enfermedad de
K1 Diagnóstico por imagen
K1 Aprendizaje automático (Inteligencia artificial)
AB Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer’s Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the construction of classification methods based on deep learning architectures applied on brain regions defined by the Automated Anatomical Labeling (AAL). Gray Matter (GM) images from each brain area have been split into 3D patches according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks. An ensemble of deep belief networks is then composed where the final prediction is determined by a voting scheme. Two deep learning based structures and four different voting schemes are implemented and compared, giving as a result a potent classification architecture where discriminative features are computed in an unsupervised fashion. The resulting method has been evaluated using a large dataset from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation prove that the proposed method is not only valid for differentiate between controls (NC) and AD images, but it also provides good performances when tested for the more challenging case of classifying Mild Cognitive Impairment (MCI) Subjects. In particular, the classification architecture provides accuracy values up to 0.90 and AUC of 0.95 for NC/AD classification, 0.84 and AUC of 0.91 for stable MCI/AD classification and 0.83 and AUC of 0.95 for NC/MCI converters classification.
PB World Scientific
YR 2016
FD 2016-08-01
LK https://hdl.handle.net/10630/28117
UL https://hdl.handle.net/10630/28117
LA eng
NO Ortiz, Andrés & Munilla, Jorge & Gorriz, Juan & Ramírez, Javier. (2016). Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease. International Journal of Neural Systems. 26. 10.1142/S0129065716500258.
NO This work was partly supported by the MICINN un der the projects TEC2012-34306 and PSI2015-65848-R, and the Consejer´ıa de Innovaci´on, Ciencia y Em presa (Junta de Andaluc´ıa, Spain) under the Ex cellence Projects P09-TIC-4530, P11-TIC-7103 andthe Universidad de M´alaga. Programa de fortalec imiento de las capacidades de I+D+I en las Uni versidades 2014-2015, de la Consejer´ıa de Econom´ıa,Innovaci´on, Ciencia y Empleo, cofinanciado por elfondo europeo de desarrollo regional (FEDER) un der the project FC14-SAF30.Data collection and sharing for this project wasfunded by the Alzheimer’s Disease Neuroimaging Ini tiative (ADNI) (National Institutes of Health GrantU01 AG024904) and DOD ADNI (Department ofDefense award number W81XWH-12-2-0012). ADNIis funded by the National Institute on Aging, theNational Institute of Biomedical Imaging and Bio engineering, and through generous contributionsfrom the following: AbbVie, Alzheimer’s Associa tion; Alzheimer’s Drug Discovery Foundation; Ara clon Biotech; BioClinica, Inc.; Biogen; Bristol-MyersSquibb Company; CereSpir, Inc.; Eisai Inc.; ElanPharmaceuticals, Inc.; Eli Lilly and Company; Eu roImmun; F. Hoffmann-La Roche Ltd and its affili ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.;Johnson &Johnson Pharmaceutical Research & DevelopmentLLC.; Lumosity ; Lundbeck; Merck & Co., Inc.;Meso Scale Diagnostics, LLC.; NeuroRx Research;Neurotrack Technologies; Novartis PharmaceuticalsCorporation; Pfizer Inc.; Piramal Imaging; Servier;Takeda Pharmaceutical Company; and TransitionTherapeutics. The Canadian Institutes of Health Re search is providing funds to support ADNI clinicalsites in Canada. Private sector contributions are fa cilitated by the Foundation for the National Insti tutes of Health (www.fnih.org). The grantee organi zation is the Northern California Institute for Re search and Education, and the study is coordinatedby the Alzheimer’s Disease Cooperative Study at theUniversity of California, San Diego. ADNI data aredisseminated by the Laboratory for Neuro Imagingat the University of Southern California.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026