ArticleScientific reports2024
A novel CNN architecture for accurate early detection and classification of Alzheimer's disease using MRI data.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 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
44 citing papers in PubMed, 1 synthesis or guideline 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
- Deep learning-based synthetic brain MRI for the assessment of regional atrophy patterns in neurodegenerative diseases.European radiology · 2026Article
- A computational model to describe multi-regional brain architecture during neurodegeneration in Alzheimer's disease.Scientific reports · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.Research square · 2026Article
- Evaluating Homoscedastic Uncertainty Weighting and Generative Tau Imputation in a Multimodal, Multitask Deep Learning Framework for Alzheimer's Disease.Neuroinformatics · 2026Article
- CROSS-ATTENTION FUSION OF STRUCTURAL AND DEFORMATION-BASED MRI FEATURES FOR ALZHEIMER'S DISEASE CLASSIFICATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- Multi-Architecture Deep Learning for Early Alzheimer's Detection in MRI: Slice- and Scan-Level Analysis.International journal of environmental research and public health · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.bioRxiv : the preprint server for biology · 2026Article
- Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques.Scientific reports · 2026Article
- Review
- GGDA-net: geometry-guided deformable attention network for Alzheimer's disease image classification.Frontiers in neuroscience · 2026Article
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction.Applications of medical artificial intelligence. AMAI (Workshop) (4th : 2024 : Taejon-si, Korea) · 2026Article
- Metaheuristic-driven dual-layer model for classifying Alzheimer's disease stages.Frontiers in computational neuroscience · 2026Article
- Detection and Classification of Alzheimer's Disease Using Deep and Machine Learning.Tomography (Ann Arbor, Mich.) · 2025Article
- Intra-scale interaction and cross-scale fusion network for detecting the progression of neurodegeneration in Alzheimer's disease.Scientific reports · 2025Article
- A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.Cognitive neurodynamics · 2025Article
- Modifier guided resilient CNN inference enables fault-tolerant edge collaboration for IoT.Scientific reports · 2025Article
- Integration of Genetic and Imaging Data for Alzheimer's Disease Diagnosis and Interpretation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer's Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a debilitating neurodegenerative disorder that requires accurate diagnosis for effective management and treatment. In this article, we propose an architecture for a convolutional neural network (CNN) that utilizes magnetic resonance imaging (MRI) data from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset to categorize AD. The network employs two separate CNN models, each with distinct filter sizes and pooling layers, which are concatenated in a classification layer. The multi-class problem is addressed across three, four, and five categories. The proposed CNN architecture achieves exceptional accuracies of 99.43%, 99.57%, and 99.13%, respectively. These high accuracies demonstrate the efficacy of the network in capturing and discerning relevant features from MRI images, enabling precise classification of AD subtypes and stages. The network architecture leverages the hierarchical nature of convolutional layers, pooling layers, and fully connected layers to extract both local and global patterns from the data, facilitating accurate discrimination between different AD categories. Accurate classification of AD carries significant clinical implications, including early detection, personalized treatment planning, disease monitoring, and prognostic assessment. The reported accuracy underscores the potential of the proposed CNN architecture to assist medical professionals and researchers in making precise and informed judgments regarding AD patients.
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.