ArticleScientific reports2023
Alzheimer's disease: new insight in assessing of amyloid plaques morphologies using multifractal geometry based on Naive Bayes optimized by random forest algorithm.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Amyloid-β plaque diversity across brain regions in Alzheimer's disease and its relation to major clinicopathological traits.Acta neuropathologica · 2026Article
- Clinical and pathologic correlations of machine learning quantification of Aβ deposits across 3 brain regions of decedents with Alzheimer disease.Journal of neuropathology and experimental neurology · 2026Article
- Oligodendrocyte precursor cells-microglia crosstalk via BMP4 drives microglial neuroprotective response and mitigates Alzheimer's disease.Signal transduction and targeted therapy · 2026Article
- Single Plaque Proteomics Reveals the Composition and Dynamics of the Amyloid Microenvironment in Alzheimer's Disease.bioRxiv : the preprint server for biology · 2026Article
- Article
- Automated Segmentation and Morphometric Analysis of Thioflavin-S-Stained Amyloid Deposits in Alzheimer's Disease Brains and Age-Matched Controls Using Weakly Supervised Deep Learning.International journal of molecular sciences · 2025Article
- Non-coding RNAs in the pathogenesis of Alzheimer's disease: β-amyloid aggregation, Tau phosphorylation and neuroinflammation.Molecular biology reports · 2025Review
- Article
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4 authors.
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Abstract
Alzheimer's disease (AD) is a physical illness, which damages a person's brain; it is the most common cause of dementia. AD can be characterized by the formation of amyloid-beta (Aβ) deposits. They exhibit diverse morphologies that range from diffuse to dense-core plaques. Most of the histological images cannot be described precisely by traditional geometry or methods. Therefore, this study aims to employ multifractal geometry in assessing and classifying amyloid plaque morphologies. The classification process is based on extracting the most descriptive features related to the amyloid-beta (Aβ) deposits using the Naive Bayes classifier. To eliminate the less important features, the Random Forest algorithm has been used. The proposed methodology has achieved an accuracy of 99%, sensitivity of 100%, and specificity of 98.5%. This study employed a new dataset that had not been widely used before.
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