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

By A. Ortiz, J. Munilla, J. Górriz, J. Ramírez. International Journal of Neural Systems, 2016

340 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/5b486ddeaa7a1e7cfa3686c25afe3330937513a5

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Olivier Colliot

Citations in the corpus

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, methodology): “It has been used in many other diverse complicated machine learning applications such as early diagnosis of the Alzheimer’s disease [13], prediction of sale prices of real estate units [14] , estimation of concrete compressive strength [15].”
A comprehensive review on ensemble deep learning: Opportunities and challenges (2023, background): “The authors of Ortiz et al. (2016) applied voting based on deep belief networks using a large dataset from the Alzheimer’s disease Neuroimaging Initiative (ADNI) (Hinrichs et al., 2009).”
Preserving differential privacy in convolutional deep belief networks (2017, background): “2010), Alzheimer’s diagnosis (Liu et al. 2014; Ortiz et al. 2016), risk prediction for chronic kidney disease progression (Perotte et al.”
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, background): “Several studies exploit multi-modal data (Aderghal et al., 2018; Cheng and Liu, 2017; Esmaeilzadeh et al., 2018; Li et al., 2015; Liu et al., 2016, 2015; Manhua Liu et al., 2018; Mingxia Liu et al., 2018b; Lu et al., 2018; Ning et al., 2018; Ortiz et al., 2016; Qiu et al., 2018; Raut and Dalal, 201”