Early Diagnosis of Alzheimer's Disease Using Deep Learning

2019 ◽  
Author(s):  
Huanhuan Ji ◽  
Zhenbing Liu ◽  
Wei Qi Yan ◽  
Reinhard Klette
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Diagnosis

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

scholarly journals Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer’s Disease Based on Deep Learning and Hybrid Methods

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2860
Author(s):  
Badiea Abdulkarem Mohammed ◽  
Ebrahim Mohammed Senan ◽  
Taha H. Rassem ◽  
Nasrin M. Makbol ◽  
Adwan Alownie Alanazi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Random Forest ◽  
Early Diagnosis ◽  
High Performance ◽  
Hybrid Methods ◽  
Machine Learning Algorithms ◽  
Hybrid Techniques

Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between neurons in the brain. So far, no effective medications have been discovered for dementia and Alzheimer’s disease. Thus, early diagnosis is necessary to avoid the development of these diseases. In this study, efficient machine learning algorithms were assessed to evaluate the Open Access Series of Imaging Studies (OASIS) dataset for dementia diagnosis. Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer’s disease. For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the low-dimensional space. All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia. The random forest algorithm achieved an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively. The second dataset, the MRI image dataset, was evaluated by AlexNet and ResNet-50 models and AlexNet+SVM and ResNet-50+SVM hybrid techniques. All models achieved high performance, but the performance of the hybrid methods between deep learning and machine learning was better than that of the deep learning models. The AlexNet+SVM hybrid model achieved accuracy, sensitivity, specificity and AUC scores of 94.8%, 93%, 97.75% and 99.70%, respectively.

Sign in / Sign up

Export Citation Format

Share Document