ArticleBrain sciences2019
Classification of Alzheimer's Disease with and without Imagery using Gradient Boosted Machines and ResNet-50.
Article in Brain sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- [Research on the application of convolution neural network in the diagnosis of Alzheimer's disease].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Pooled it
- AlzhiNet: Traversing from 2D-CNN to 3D-CNN, Towards Early Detection and Diagnosis of Alzheimer's Disease.Interdisciplinary sciences, computational life sciences · 2026Article
- Hybrid Vision Transformer-CNN Framework for Alzheimer's Disease Cell Type Classification: A Comparative Study with Vision-Language Models.Journal of imaging · 2026Article
- Fusion of transfer learning models for detection of alzheimer's disease using bidirectional long short-term memory with equilibrium optimization algorithm.Scientific reports · 2025Article
- Predicting High Urinary Tract Infection Rates in Skilled Nursing Facilities: A Machine Learning Approach.Healthcare (Basel, Switzerland) · 2025Article
- Diagnosis of Alzheimer's disease using transfer learning with multi-modal 3D Inception-v4.Quantitative imaging in medicine and surgery · 2025Article
- Machine Learning-Based Alzheimer's Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation.Diagnostics (Basel, Switzerland) · 2025Article
- AD-Diff: enhancing Alzheimer's disease prediction accuracy through multimodal fusion.Frontiers in computational neuroscience · 2025Article
- Advancing Alzheimer's Disease Diagnosis Using VGG19 and XGBoost: A Neuroimaging-Based Method.Current Alzheimer research · 2025Article
- AlzheimerViT: harnessing lightweight vision transformer architecture for proactive Alzheimer's screening.Frontiers in medicine · 2025Article
- Etiology of Late-Onset Alzheimer's Disease, Biomarker Efficacy, and the Role of Machine Learning in Stage Diagnosis.Diagnostics (Basel, Switzerland) · 2024Review
- White blood cells classification using multi-fold pre-processing and optimized CNN model.Scientific reports · 2024Article
- ANFIS Fuzzy convolutional neural network model for leaf disease detection.Frontiers in plant science · 2024Article
- Brain MRI sequence and view plane identification using deep learning.Frontiers in neuroinformatics · 2024Article
- Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline.Cureus · 2023Review
- Prediction of Prednisolone Dose Correction Using Machine Learning.Journal of healthcare informatics research · 2023Article
- An explainable artificial intelligence system for diagnosingTherapeutic advances in gastroenterology · 2023Article
- Logistic random forest boosting technique for Alzheimer's diagnosis.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2023Article
- Predicting the severity of white matter lesions among patients with cerebrovascular risk factors based on retinal images and clinical laboratory data: a deep learning study.Frontiers in neurology · 2023Article
- Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery.Plants (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundAlzheimer's is a disease for which there is no cure. Diagnosing Alzheimer's disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI's (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.
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