ArticleApplied bionics and biomechanics2021
Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network.
Article in Applied bionics and biomechanics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.
- Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks.Human brain mapping · 2023Pooled it
- Insertion Network for Image Sequence Correspondence Building.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Recent Advancements in Neuroimaging-Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e-Health: A Systematic Review.Health science reports · 2025Review
- A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.Scientific reports · 2024Article
- Fractional gradient optimized explainable convolutional neural network for Alzheimer's disease diagnosis.Heliyon · 2024Article
- Engineered macrophage-biomimetic versatile nanoantidotes for inflammation-targeted therapy against Alzheimer's disease by neurotoxin neutralization and immune recognition suppression.Bioactive materials · 2023Article
- Hippocampus Segmentation-Based Alzheimer's Disease Diagnosis and Classification of MRI Images.Arabian journal for science and engineering · 2023Article
- Retracted: Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network.Applied bionics and biomechanics · 2023Article
- A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm.Scientific reports · 2022Article
- A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications.Brain sciences · 2022Review
- Large Margin and Local Structure Preservation Sparse Representation Classifier for Alzheimer's Magnetic Resonance Imaging Classification.Frontiers in aging neuroscience · 2022Article
- A boon to aged society: Early diagnosis of Alzheimer's disease-An opinion.Frontiers in public health · 2022Article
- Effect of data leakage in brain MRI classification using 2D convolutional neural networks.Scientific reports · 2021Article
- Diagnosis of Alzheimer's Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN).Computational and mathematical methods in medicine · 2021Article
Corrections and comments
- Retraction · 2023-10-11Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Informed/Patient Consent - None/Withdrawn · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
There are many kinds of brain abnormalities that cause changes in different parts of the brain. Alzheimer's disease is a chronic condition that degenerates the cells of the brain leading to memory asthenia. Cognitive mental troubles such as forgetfulness and confusion are one of the most important features of Alzheimer's patients. In the literature, several image processing techniques, as well as machine learning strategies, were introduced for the diagnosis of the disease. This study is aimed at recognizing the presence of Alzheimer's disease based on the magnetic resonance imaging of the brain. We adopted a deep learning methodology for the discrimination between Alzheimer's patients and healthy patients from 2D anatomical slices collected using magnetic resonance imaging. Most of the previous researches were based on the implementation of a 3D convolutional neural network, whereas we incorporated the usage of 2D slices as input to the convolutional neural network. The data set of this research was obtained from the OASIS website. We trained the convolutional neural network structure using the 2D slices to exhibit the deep network weightings that we named as the Alzheimer Network (AlzNet). The accuracy of our enhanced network was 99.30%. This work investigated the effects of many parameters on AlzNet, such as the number of layers, number of filters, and dropout rate. The results were interesting after using many performance metrics for evaluating the proposed AlzNet.
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.