ArticleFrontiers in computational neuroscience2017
Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning.
Article in Frontiers in computational neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Article
- A review of decomposition methods for brain states estimation.Biomedical engineering online · 2026Review
- From Molecules to Medicines: The Role of AI-Driven Drug Discovery Against Alzheimer's Disease and Other Neurological Disorders.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Standardized operating room nursing care is effective in preventing the occurrence of surgical wound infections.American journal of translational research · 2025Article
- Alzheimer's disease with depressive symptoms: Clinical effect of intermittent theta burst stimulation repetitive transcranial magnetic stimulation.World journal of psychiatry · 2024Article
- Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences From Three fMRI Paradigms.IEEE transactions on bio-medical engineering · 2023Article
- Machine Learning on Visibility Graph Features Discriminates the Cognitive Event-Related Potentials of Patients with Early Alzheimer's Disease from Healthy Aging.Brain sciences · 2023Article
- The Use of Artificial Intelligence in the Management of Neurodegenerative Disorders; Focus on Alzheimer's Disease.Galen medical journal · 2023Article
- Clinical efficacy of nutritional diet therapy on gestational diabetes mellitus.American journal of translational research · 2022Article
- Article
- Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson's Disease.Frontiers in neuroinformatics · 2018Article
- Classification of Alzheimer's Disease, Mild Cognitive Impairment and Normal Control Subjects Using Resting-State fMRI Based Network Connectivity Analysis.IEEE journal of translational engineering in health and medicine · 2018Article
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Abstract
Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were all included for classification of AD vs. CU, MCI vs. CU, as well as AD vs. MCI (113 AD patients, 110 MCI patients, and 117 CU subjects). By adopting MKSCDDL, we achieved a classification accuracy of 98.18% for AD vs. CU, 78.50% for MCI vs. CU, and 74.47% for AD vs. MCI, which in each instance was superior to results obtained using several other state-of-the-art approaches (MKL, JRC, mSRC, and mSCDDL). In addition, testing time results outperformed other high quality methods. Therefore, the results suggested that the MKSCDDL procedure is a promising tool for assisting early diagnosis of diseases using neuroimaging data.
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