ArticleSensors (Basel, Switzerland)2023
Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.Journal of medical systems · 2024Pooled it
- X-ViTCNN: A Novel Network-Level Fusion of Transfer Learning and Customized Vision Transformer for Multi-Stage Alzheimer's Disease Prediction Using MRI Scans.Diagnostics (Basel, Switzerland) · 2026Article
- 3DViT-GAT: a unified atlas-based 3D vision transformer and graph learning framework for major depressive disorder detection using structural MRI data.Scientific reports · 2026Article
- Enhancing Alzheimer's disease classification with a transformer-based model using self-supervised learning.Scientific reports · 2026Article
- Multimodal classification of mood disorders using structural MRI and polygenic risk scores: a machine learning ablation study in UK Biobank.Frontiers in psychiatry · 2026Article
- Vision and convolutional transformers for Alzheimer's disease diagnosis: a systematic review of architectures, multimodal fusion and critical gaps.Brain informatics · 2025Review
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Comparative analysis of multiple deep learning models with mitigation-driven approaches for enhanced Alzheimer's disease classification.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
- Potential of AI-based diagnostic grading system for knee osteoarthritis.Frontiers in medicine · 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
- Hybrid-RViT: Hybridizing ResNet-50 and Vision Transformer for Enhanced Alzheimer's disease detection.PloS one · 2025Article
- Multi-model deep learning for dementia detection: addressing data and model limitations.Frontiers in neuroscience · 2025Article
- Psychiatric disorders from EEG signals through deep learning models.IBRO neuroscience reports · 2024Article
- Fractional gradient optimized explainable convolutional neural network for Alzheimer's disease diagnosis.Heliyon · 2024Article
- Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer's Disease Detection.Journal of imaging · 2024Article
- Automated Ischemic Stroke Classification from MRI Scans: Using a Vision Transformer Approach.Journal of clinical medicine · 2024Article
- Overcoming the Black Box Challenge: Building Trust in Artificial Intelligence Algorithms in Oncology.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Alzheimer's disease (AD) is the most common form of dementia. Computer-aided diagnosis (CAD) can help in the early detection of associated cognitive impairment. The aim of this work is to improve the automatic detection of dementia in MRI brain data. For this purpose, we used an established pipeline that includes the registration, slicing, and classification steps. The contribution of this research was to investigate for the first time, to our knowledge, three current and promising deep convolutional models (ResNet, DenseNet, and EfficientNet) and two transformer-based architectures (MAE and DeiT) for mapping input images to clinical diagnosis. To allow a fair comparison, the experiments were performed on two publicly available datasets (ADNI and OASIS) using multiple benchmarks obtained by changing the number of slices per subject extracted from the available 3D voxels. The experiments showed that very deep ResNet and DenseNet models performed better than the shallow ResNet and VGG versions tested in the literature. It was also found that transformer architectures, and DeiT in particular, produced the best classification results and were more robust to the noise added by increasing the number of slices. A significant improvement in accuracy (up to 7%) was achieved compared to the leading state-of-the-art approaches, paving the way for the use of CAD approaches in real-world applications.
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.