ArticleHealthcare (Basel, Switzerland)2023
Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer's Disease.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 27 citations in OpenAlex.
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- Vision and convolutional transformers for Alzheimer's disease diagnosis: a systematic review of architectures, multimodal fusion and critical gaps.Brain informatics · 2025Review
- Early detection of Alzheimer's disease progression stages using hybrid of CNN and transformer encoder models.Scientific reports · 2025Article
- Stacked CNN-based multichannel attention networks for Alzheimer disease detection.Scientific reports · 2025Article
- Hybrid-RViT: Hybridizing ResNet-50 and Vision Transformer for Enhanced Alzheimer's disease detection.PloS one · 2025Article
- A Feature-Fusion Technique-Based Alzheimer's Disease Classification Using Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Ensemble of vision transformer architectures for efficient Alzheimer's Disease classification.Brain informatics · 2024Article
- Automated Ischemic Stroke Classification from MRI Scans: Using a Vision Transformer Approach.Journal of clinical medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Alzheimer's disease is a common neurological disorder and mental disability that causes memory loss and cognitive decline, presenting a major challenge to public health due to its impact on millions of individuals worldwide. It is crucial to diagnose and treat Alzheimer's in a timely manner to improve the quality of life of both patients and caregivers. In the recent past, machine learning techniques have showed potential in detecting Alzheimer's disease by examining neuroimaging data, especially Magnetic Resonance Imaging (MRI). This research proposes an attention-based mechanism that employs the vision transformer approach to detect Alzheimer's using MRI images. The presented technique applies preprocessing to the MRI images and forwards them to a vision transformer network for classification. This network is trained on the publicly available Kaggle dataset, and it illustrated impressive results with an accuracy of 99.06%, precision of 99.06%, recall of 99.14%, and F1-score of 99.1%. Furthermore, a comparative study is also conducted to evaluate the performance of the proposed method against various state-of-the-art techniques on diverse datasets. The proposed method demonstrated superior performance, outperforming other published methods when applied to the Kaggle dataset.
Indexed as
Identifiers
What OpenQuestion holds
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.