NeuroFusion-ViT: A Hybrid CNN-EVA Transformer Model with Cross-Attention Fusion for MRI-Based Alzheimer's Stage Classification

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dc.contributor.authorSoylemez, Derya Ozturk
dc.contributor.authorDogru, Sevinc Ay
dc.date.accessioned2026-04-25T14:20:27Z
dc.date.available2026-04-25T14:20:27Z
dc.date.issued2026
dc.departmentSinop Üniversitesi
dc.description.abstractBackground: Alzheimer's disease is the most common type of dementia and a progressive neurodegenerative disease that begins with neuronal damage and leads to a reduction in brain tissue. Currently, there is no cure for this disease, and existing approaches focus on alleviating symptoms. Methods: This study proposes NeuroFusion-ViT, a highly accurate and computationally efficient hybrid deep learning model for early-stage detection of Alzheimer's disease. The model combines an EVA-02-based Vision Transformer (ViT) with the ConvNeXt-Small CNN architecture, providing powerful representation learning that can process both global context and local details. The proposed Gated Cross-Attention Fusion (G-CAF) mechanism dynamically combines two different features, offering high discriminative power and model stability. Results: In experiments conducted on the OASIS MRI dataset, the model achieved 99.86% accuracy, 0.9989 Macro F1, and 0.999 ROC-AUC values, demonstrating clear superiority over single-modal and hybrid models described in the literature. Furthermore, 5-fold cross-validation results also support the model's high generalizability. Ablation studies showed that each of the components-cross-attention, gate mechanism, Dual LayerNorm, and FFN-Dropout-made a meaningful contribution to performance. Conclusions: The results demonstrate that the NeuroFusion-ViT architecture offers a reliable, stable, and clinically applicable solution for Alzheimer's stage classification.
dc.identifier.doi10.3390/diagnostics16050754
dc.identifier.issn2075-4418
dc.identifier.issue5
dc.identifier.pmid41828028
dc.identifier.scopus2-s2.0-105032695805
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics16050754
dc.identifier.urihttps://hdl.handle.net/11486/8588
dc.identifier.volume16
dc.identifier.wosWOS:001713907100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260420
dc.subjectAlzheimer
dc.subjectvision transformers
dc.subjectMRI
dc.titleNeuroFusion-ViT: A Hybrid CNN-EVA Transformer Model with Cross-Attention Fusion for MRI-Based Alzheimer's Stage Classification
dc.typeArticle

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