ArticleDiagnostics (Basel, Switzerland)2022
HTLML: Hybrid AI Based Model for Detection of Alzheimer's Disease.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines 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
- Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis.JMIR aging · 2024Pooled it
- Evaluating Homoscedastic Uncertainty Weighting and Generative Tau Imputation in a Multimodal, Multitask Deep Learning Framework for Alzheimer's Disease.Neuroinformatics · 2026Article
- Artificial intelligence-assisted neuropharmacology: reshaping drug discovery and translational strategies for neurological diseases.Turkish journal of medical sciences · 2026Review
- HyperTransFusion: a hypernetwork transformer with black winged kite optimization for multimodal early Alzheimer's disease diagnosis.Frontiers in digital health · 2026Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- An optimized hybrid deep learning model to detect Alzheimer disease.Scientific reports · 2025Article
- Artificial Intelligence in Alzheimer's Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval.Journal of clinical medicine · 2025Review
- Prediction of Alzheimer's Disease Using Modified DNN with Optimal Feature Selection Based on Seagull Optimization.Journal of imaging informatics in medicine · 2025Article
- Stacked CNN-based multichannel attention networks for Alzheimer disease detection.Scientific reports · 2025Article
- Intelligent Alzheimer's diagnosis and disability assessment: robust medical imaging analysis using ensemble learning with ResNet-50 and EfficientNet-B3.Frontiers in medicine · 2025Article
- Alzheimer's disease classification using a hybrid deep learning approach with multi-layer U-net segmentation and XAI driven analysis.PloS one · 2025Article
- Advancing Alzheimer's Disease Diagnosis Using VGG19 and XGBoost: A Neuroimaging-Based Method.Current Alzheimer research · 2025Article
- Advanced interpretable diagnosis of Alzheimer's disease using SECNN-RF framework with explainable AI.Frontiers in artificial intelligence · 2024Article
- Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA.Healthcare (Basel, Switzerland) · 2023Article
- Accurate Detection of Alzheimer's Disease Using Lightweight Deep Learning Model on MRI Data.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors.
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Abstract
Alzheimer's disease (AD) is a degenerative condition of the brain that affects the memory and reasoning abilities of patients. Memory is steadily wiped out by this condition, which gradually affects the brain's ability to think, recall, and form intentions. In order to properly identify this disease, a variety of manual imaging modalities including CT, MRI, PET, etc. are being used. These methods, however, are time-consuming and troublesome in the context of early diagnostics. This is why deep learning models have been devised that are less time-intensive, require less high-tech hardware or human interaction, continue to improve in performance, and are useful for the prediction of AD, which can also be verified by experimental results obtained by doctors in medical institutions or health care facilities. In this paper, we propose a hybrid-based AI-based model that includes the combination of both transfer learning (TL) and permutation-based machine learning (ML) voting classifier in terms of two basic phases. In the first phase of implementation, it comprises two TL-based models: namely, DenseNet-121 and Densenet-201 for features extraction, whereas in the second phase of implementation, it carries out three different ML classifiers like SVM, Naïve base and XGBoost for classification purposes. The final classifier outcomes are evaluated by means of permutations of the voting mechanism. The proposed model achieved accuracy of 91.75%, specificity of 96.5%, and an F1-score of 90.25. The dataset used for training was obtained from Kaggle and contains 6200 photos, including 896 images classified as mildly demented, 64 images classified as moderately demented, 3200 images classified as non-demented, and 1966 images classified as extremely mildly demented. The results show that the suggested model outperforms current state-of-the-art models. These models could be used to generate therapeutically viable methods for detecting AD in MRI images based on these results for clinical prospective.
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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.