ArticleBrain sciences2020
A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 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
- Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.Frontiers in medicine · 2026Review
- AD diagnosis model based on fusion of heterogeneous brain imaging and genomic data.Frontiers in neuroscience · 2026Article
- Lightweight Vision Transformer with transfer learning for interpretable Alzheimer's disease severity assessment.Scientific reports · 2025Article
- Trustworthy pneumonia detection in chest X-ray imaging through attention-guided deep learning.Scientific reports · 2025Article
- Performance evaluation of enhanced deep learning classifiers for person identification and gender classification.Scientific reports · 2025Article
- Alzheimer's Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network.Diagnostics (Basel, Switzerland) · 2025Article
- An MRI based histogram oriented gradient and deep learning approach for accurate classification of mild cognitive impairment and Alzheimer's disease.Frontiers in medicine · 2025Article
- Advancing Alzheimer's Disease Diagnosis Using VGG19 and XGBoost: A Neuroimaging-Based Method.Current Alzheimer research · 2025Article
- TIRESIA and TISBE: Explainable Artificial Intelligence Based Web Platforms for the Transparent Assessment of the Developmental Toxicity of Chemicals and Drugs.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Optimizing a 3D convolutional neural network to detect Alzheimer's disease based on MRI.PeerJ. Computer science · 2025Article
- A hybrid transformer-based approach for early detection of Alzheimer's disease using MRI images.BioImpacts : BI · 2025Article
- Connectome-based prediction of future episodic memory performance for individual amnestic mild cognitive impairment patients.Brain communications · 2025Article
- Optimized Hybrid Deep Learning Framework for Early Detection of Alzheimer's Disease Using Adaptive Weight Selection.Diagnostics (Basel, Switzerland) · 2024Article
- A multi-slice attention fusion and multi-view personalized fusion lightweight network for Alzheimer's disease diagnosis.BMC medical imaging · 2024Article
- Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.European heart journal. Digital health · 2024Article
- Article
- PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI.Journal of digital imaging · 2023Article
- Automatic pavement texture recognition using lightweight few-shot learning.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
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.
Funding
Abstract
Alzheimer's disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer's disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.
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.