ArticlePeerJ2015
Detection of Alzheimer's disease by displacement field and machine learning.
Article in PeerJ, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- The presymptomatic treatment with 3HFWC nanosubstance decreased plaque load in 5XFAD mouse model of Alzheimer's disease.CNS neuroscience & therapeutics · 2024Article
- Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis.Neurobiology of disease · 2023Article
- Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment.Bioengineering (Basel, Switzerland) · 2023Article
- An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease.Frontiers in aging neuroscience · 2023Article
- Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer's Disease.Frontiers in aging neuroscience · 2022Article
- Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients.Computational intelligence and neuroscience · 2022Article
- Research on Voxel-Based Features Detection and Analysis of Alzheimer's Disease Using Random Survey Support Vector Machine.Frontiers in neuroinformatics · 2022Article
- Deep learning prediction of mild cognitive impairment conversion to Alzheimer's disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI.PeerJ. Computer science · 2021Article
- ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation.Frontiers in aging neuroscience · 2021Article
- A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease.Brain sciences · 2020Article
- Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification.Frontiers in cell and developmental biology · 2020Article
- Deep learning-based classification of multi-categorical Alzheimer's disease data.Current neurobiology · 2019Article
- Automated Detection of Alzheimer's Disease Using Brain MRI Images- A Study with Various Feature Extraction Techniques.Journal of medical systems · 2019Article
- Classification of Alzheimer's Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling.Journal of medical systems · 2018Article
- The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1.Journal of Alzheimer's disease : JAD · 2018Article
- Article
- Diagnosis of Alzheimer's Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network.Journal of healthcare engineering · 2017Article
- Twin SVM-Based Classification of Alzheimer's Disease Using Complex Dual-Tree Wavelet Principal Coefficients and LDA.Journal of healthcare engineering · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Aim. Alzheimer's disease (AD) is a chronic neurodegenerative disease. Recently, computer scientists have developed various methods for early detection based on computer vision and machine learning techniques. Method. In this study, we proposed a novel AD detection method by displacement field (DF) estimation between a normal brain and an AD brain. The DF was treated as the AD-related features, reduced by principal component analysis (PCA), and finally fed into three classifiers: support vector machine (SVM), generalized eigenvalue proximal SVM (GEPSVM), and twin SVM (TSVM). The 10-fold cross validation repeated 50 times. Results. The results showed the "DF + PCA + TSVM" achieved the accuracy of 92.75 ± 1.77, sensitivity of 90.56 ± 1.15, specificity of 93.37 ± 2.05, and precision of 79.61 ± 2.21. This result is better than or comparable with not only the other proposed two methods, but also ten state-of-the-art methods. Besides, our method discovers the AD is related to following brain regions disclosed in recent publications: Angular Gyrus, Anterior Cingulate, Cingulate Gyrus, Culmen, Cuneus, Fusiform Gyrus, Inferior Frontal Gyrus, Inferior Occipital Gyrus, Inferior Parietal Lobule, Inferior Semi-Lunar Lobule, Inferior Temporal Gyrus, Insula, Lateral Ventricle, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterior Cingulate, Precentral Gyrus, Precuneus, Sub-Gyral, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, and Uncus. Conclusion. The displacement filed is effective in detection of AD and related brain-regions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.