ArticleIEEE transactions on computational social systems2024
Adaptive 3DCNN-based Interpretable Ensemble Model for Early Diagnosis of Alzheimer's Disease.
Article in IEEE transactions on computational social systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computer-Aided Decision Support Systems of Alzheimer's Disease Diagnosis - A Systematic Review.Current medical imaging · 2025Pooled it
- Characteristics of brain glucose metabolism in Parkinson's disease patients with freezing of gait: a study based onBMC neurology · 2025Article
- A novel approach hybrid of ensemble learning and 3-D CNN mechanism: early-stage diagnosis of Alzheimer's disease using EEG signals.Scientific reports · 2025Article
- Ottawa ankle and foot rules in China: applicability in a defensive environment.European journal of medical research · 2025Article
- Novel hippocampus-centered methodology for informative instance selection in Alzheimer's disease data.Heliyon · 2024Article
- [An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention.IEEE journal of biomedical and health informatics · 2024Article
- AMSF: attention-based multi-view slice fusion for early diagnosis of Alzheimer's disease.PeerJ. Computer science · 2023Article
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Authors and funding
9 authors.
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Abstract
Adaptive interpretable ensemble model based on three-dimensional Convolutional Neural Network (3DCNN) and Genetic Algorithm (GA), i.e., 3DCNN+EL+GA, was proposed to differentiate the subjects with Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) and further identify the discriminative brain regions significantly contributing to the classifications in a data-driven way. Plus, the discriminative brain sub-regions at a voxel level were further located in these achieved brain regions, with a gradient-based attribution method designed for CNN. Besides disclosing the discriminative brain sub-regions, the testing results on the datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS) indicated that 3DCNN+EL+GA outperformed other state-of-the-art deep learning algorithms and that the achieved discriminative brain regions (e.g., the rostral hippocampus, caudal hippocampus, and medial amygdala) were linked to emotion, memory, language, and other essential brain functions impaired early in the AD process. Future research is needed to examine the generalizability of the proposed method and ideas to discern discriminative brain regions for other brain disorders, such as severe depression, schizophrenia, autism, and cerebrovascular diseases, using neuroimaging.
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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.