Early Diagnosis of Alzheimer's Disease: A Neuroimaging Study with Deep Learning Architectures

2018 ◽  
Author(s):  
Jyoti Islam ◽  
Yanqing Zhang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Neuroimaging Study ◽  
Learning Architectures

Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease

2016 ◽  
Vol 26 (07) ◽  
pp. 1650025 ◽  
Author(s):  
Andrés Ortiz ◽  
Jorge Munilla ◽  
Juan M. Górriz ◽  
Javier Ramírez
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Brain Area ◽  
Brain Regions ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Learning Architectures ◽  
Aided Diagnosis

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.

scholarly journals Applying a deep learning approach focusing on spatiotemporal features in early diagnosis of Alzheimer's disease

2021 ◽  
Vol 17 (S12) ◽  
Author(s):  
Eyitomilayo Yemisi Babatope ◽  
Jesus Alejandro Acosta‐Franco ◽  
Mireya Saraí García‐Vázquez ◽  
Alejandro Álvaro Ramírez‐Acosta ◽  
APIM Laboratory Citedi‐IPN
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Learning Approach ◽  
Spatiotemporal Features

Deep learning for early diagnosis of Alzheimer’s disease: a contribution and a brief review

2020 ◽  
pp. 63-78
Author(s):  
Iago Richard Rodrigues da Silva ◽  
Gabriela dos Santos Lucas e Silva ◽  
Rodrigo Gomes de Souza ◽  
Maíra Araújo de Santana ◽  
Washington Wagner Azevedo da Silva ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Diagnosis
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