ArticlePloS one2019
The use of back propagation neural networks and 18F-Florbetapir PET for early detection of Alzheimer's disease using Alzheimer's Disease Neuroimaging Initiative database.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 26 citations in OpenAlex.
- Optimized Convolutional Fusion for Multimodal Neuroimaging in Alzheimer's Disease Diagnosis: Enhancing Data Integration and Feature Extraction.Journal of personalized medicine · 2023Article
- Efficient Training on Alzheimer's Disease Diagnosis with Learnable Weighted Pooling for 3D PET Brain Image Classification.Electronics · 2023Article
- Brain tumor segmentation in MRI images using nonparametric localization and enhancement methods with U-net.International journal of computer assisted radiology and surgery · 2022Article
- Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection.Computational intelligence and neuroscience · 2021Review
- Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks.SLAS technology · 2020Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Amyloid beta (Aβ) plaques aggregation is considered as the "start" of the degenerative process that manifests years before the clinical symptoms appear in Alzheimer's Disease (AD). The aim of this study is to use back propagation neural networks (BPNNs) in 18F-florbetapir PET data for automated classification of four patient groups including AD, late mild cognitive impairment (LMCI), early mild cognitive impairment (EMCI), and significant memory concern (SMC), versus normal control (NC) for early AD detection. Five hundred images for AD, LMCI, EMCI, SMC, and NC, i.e., 100 images for each group, were used from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results showed that the automated classification of NC/AD produced a high accuracy of 87.9%, while the results for the prodromal stages of the disease were 66.4%, 60.0%, and 52.9% for NC/LCMI, NC/EMCI and NC/SMC, respectively. The proposed method together with the image preparation steps can be used for early AD detection and classification with high accuracy using Aβ PET dataset.
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Registered trials
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