ArticleJournal of healthcare engineering2017
Diagnosis of Alzheimer's Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features.
Article in Journal of healthcare engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it, 140 citations in OpenAlex.
- 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
- Network analysis of optimal deep or machine learning strategies for classification and detection of Alzheimer's disease based on MRI scanning.Frontiers in neuroscience · 2026Pooled it
- Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.Journal of medical systems · 2019Pooled it
- TA-SSM net: tri-directional attention and structured state-space model for enhanced MRI-Based diagnosis of Alzheimer's disease and mild cognitive impairment.BMC medical imaging · 2025Article
- Fuzzy Optimized Attention Network with Multi-Instance Deep Learning (FOAN-MIDL) for Alzheimer's Disease Diagnosis with Structural Magnetic Resonance Imaging (sMRI).Diagnostics (Basel, Switzerland) · 2025Article
- Implications of Convolutional Neural Network for Brain MRI Image Classification to Identify Alzheimer's Disease.Parkinson's disease · 2024Article
- Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment.Scientific reports · 2023Article
- Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium.NeuroImage · 2023Article
- Application of machine learning in dementia diagnosis: A systematic literature review.Heliyon · 2023Article
- Predicting cognitive decline in a low-dimensional representation of brain morphology.Scientific reports · 2023Article
- Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA.Healthcare (Basel, Switzerland) · 2023Article
- Skin Lesion Segmentation Using an Ensemble of Different Image Processing Methods.Diagnostics (Basel, Switzerland) · 2023Article
- Advanced Overview of Biomarkers and Techniques for Early Diagnosis of Alzheimer's Disease.Cellular and molecular neurobiology · 2023Review
- Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer's Disease.Genes · 2023Article
- An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease.Frontiers in aging neuroscience · 2023Article
- A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders.Biology · 2022Review
- Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer's Disease.Life (Basel, Switzerland) · 2022Review
- Article
- Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia.Sensors (Basel, Switzerland) · 2021Article
- Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques.Journal of Alzheimer's disease reports · 2021Article
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4 authors at 3 institutions in 1 country.
Funding
Abstract
Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer's disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.
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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.