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October 17, 2025BioengineeringOpen Access

NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer’s Disease Diagnosis

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Proposed multimodal approach enhances accuracy of Alzheimer’s disease classification, indicating its potential impact on early diagnosis.

NeuroNet-AD achieved 98.68% accuracy in classifying Alzheimer's disease stages, showcasing its diagnostic potential.
Evaluated on the ADNI dataset with 5-fold cross-validation, it demonstrated robustness across various settings.
The model integrates MRI images and clinical data, enhancing classification through advanced multimodal techniques.
External validation on the OASIS-3 dataset confirmed the model’s effectiveness, with high accuracy across demographic variances.

Rahman et al. (2025) studied this question.

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October 17, 2025BioengineeringOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Proposed multimodal approach enhances accuracy of Alzheimer’s disease classification, indicating its potential impact on early diagnosis.

NeuroNet-AD achieved 98.68% accuracy in classifying Alzheimer's disease stages, showcasing its diagnostic potential.
Evaluated on the ADNI dataset with 5-fold cross-validation, it demonstrated robustness across various settings.
The model integrates MRI images and clinical data, enhancing classification through advanced multimodal techniques.
External validation on the OASIS-3 dataset confirmed the model’s effectiveness, with high accuracy across demographic variances.

Rahman et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark