ArticleJournal of healthcare engineering2022
On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer's Disease Using Neuroimaging Modalities and Data Augmentation Methods.
Article in Journal of healthcare engineering, 2022. 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 It has been retracted, and should not be counted.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 10 papers.
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.
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Who cites it
10 citing papers in PubMed.
- The Feasibility of Early Alzheimer's Disease Diagnosis Using a Neural Network Hybrid Platform.Biosensors · 2022Trial
- Inductive and Transfer Learning-Based Hybrid Model Techniques for Accurate and Automated Diagnosis of Neurological Diseases.Brain and behavior · 2025Article
- Recent Advancements in Neuroimaging-Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e-Health: A Systematic Review.Health science reports · 2025Review
- Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review.Health science reports · 2024Review
- Improved Support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification.PloS one · 2024Article
- Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions.Bioengineering (Basel, Switzerland) · 2023Review
- A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification.Sensors (Basel, Switzerland) · 2023Article
- Retracted: On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer's Disease Using Neuroimaging Modalities and Data Augmentation Methods.Journal of healthcare engineering · 2023Article
- Early-Stage Alzheimer's Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains.Sensors (Basel, Switzerland) · 2022Article
- An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health.Computational intelligence and neuroscience · 2022Article
Corrections and comments
- Retraction · 2023-10-04Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is an irreversible illness of the brain impacting the functional and daily activities of elderly population worldwide. Neuroimaging sensory systems such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) measure the pathological changes in the brain associated with this disorder especially in its early stages. Deep learning (DL) architectures such as Convolutional Neural Networks (CNNs) are successfully used in recognition, classification, segmentation, detection, and other domains for data interpretation. Data augmentation schemes work alongside DL techniques and may impact the final task performance positively or negatively. In this work, we have studied and compared the impact of three data augmentation techniques on the final performances of CNN architectures in the 3D domain for the early diagnosis of AD. We have studied both binary and multiclass classification problems using MRI and PET neuroimaging modalities. We have found the performance of random zoomed in/out augmentation to be the best among all the augmentation methods. It is also observed that combining different augmentation methods may result in deteriorating performances on the classification tasks. Furthermore, we have seen that architecture engineering has less impact on the final classification performance in comparison to the data manipulation schemes. We have also observed that deeper architectures may not provide performance advantages in comparison to their shallower counterparts. We have further observed that these augmentation schemes do not alleviate the class imbalance issue.
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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.