An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis

This work introduces a clinically promising multimodal AI framework that enhances diagnostic accuracy while maintaining transparency through explainable techniques. NeuroFusion-ADNet sets a foundation for the development of efficient, interpretable and deployable tools in the early diagnosis of AD.

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Publication Details

Journal
Digital Health
Published
2025-05-01
DOI
https://doi.org/10.1177/20552076251390281
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis

Abdullah Alsaleh
Digital Health
Machine Learning in Healthcare
article

An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis

Abdullah Alsaleh
article en

Abstract

This work introduces a clinically promising multimodal AI framework that enhances diagnostic accuracy while maintaining transparency through explainable techniques. NeuroFusion-ADNet sets a foundation for the development of efficient, interpretable and deployable tools in the early diagnosis of AD.

Digital HealthVol. 11
Majmaah University (SA)
Openalex Percentile: Top 84%
Machine Learning in Healthcare
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An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis — Abdullah Alsaleh · Digital Health (2025) | TGRS Research Map | TGRS