ArticleSensors (Basel, Switzerland)2022
Early-Stage Alzheimer's Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Improved YOLOv8 for early detection of Alzheimer's disease from magnetic resonance imaging.Frontiers in aging neuroscience · 2026Article
- Early diagnosis of alzheimer's disease using PET imaging and deep learning with comparative data augmentation techniques.Scientific reports · 2025Article
- Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer's disease diagnosis.Scientific reports · 2025Article
- Efficient diagnosis of diabetes mellitus using an improved ensemble method.Scientific reports · 2025Article
- Article
- Advancements in deep learning for early diagnosis of Alzheimer's disease using multimodal neuroimaging: challenges and future directions.Frontiers in neuroinformatics · 2025Review
- Article
- Article
- Deep learning to combat knee osteoarthritis and severity assessment by using CNN-based classification.BMC musculoskeletal disorders · 2024Article
- Article
- Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review.Health science reports · 2024Review
- High-Precision Skin Disease Diagnosis through Deep Learning on Dermoscopic Images.Bioengineering (Basel, Switzerland) · 2024Article
- Alzheimer Disease Detection Studies: Perspective on Multi-Modal Data.Yearbook of medical informatics · 2024Review
- A bilateral filtering-based image enhancement for Alzheimer disease classification using CNN.PloS one · 2024Article
- Automatic Analysis of MRI Images for Early Prediction of Alzheimer's Disease Stages Based on Hybrid Features of CNN and Handcrafted Features.Diagnostics (Basel, Switzerland) · 2023Article
- Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning.Computational intelligence and neuroscience · 2022Article
- Explainable bidirectional encoder representations from image transformers for Alzheimer's disease prediction.Digital healthArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Alzheimer's Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later stages of the disease. Neuroimaging modalities are routinely used in clinical practice to capture brain alterations associated with AD. On the other hand, deep learning methods are routinely used to recognize patterns in underlying data distributions effectively. This work uses Convolutional Neural Network (CNN) architectures in both 2D and 3D domains to classify the initial stages of AD into AD, Mild Cognitive Impairment (MCI) and Normal Control (NC) classes using the positron emission tomography neuroimaging modality deploying data augmentation in a random zoomed in/out scheme. We used novel concepts such as the blurring before subsampling principle and distant domain transfer learning to build 2D CNN architectures. We performed three binaries, that is, AD/NC, AD/MCI, MCI/NC and one multiclass classification task AD/NC/MCI. The statistical comparison revealed that 3D-CNN architecture performed the best achieving an accuracy of 89.21% on AD/NC, 71.70% on AD/MCI, 62.25% on NC/MCI and 59.73% on AD/NC/MCI classification tasks using a five-fold cross-validation hyperparameter selection approach. Data augmentation helps in achieving superior performance on the multiclass classification task. The obtained results support the application of deep learning models towards early recognition of AD.
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What Socratic 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 Socratic graph.