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From neuroimages to insights: Artificial intelligence-powered hybrid models for Alzheimer’s disease detection

Gracy Singh1* ,  Nidhi Verma1 ,  Sonali Bhatt2 ,  Saurabh Mukherjee3
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1 Centre of Artificial Intelligence, Banasthali Vidyapith, Tonk, Rajasthan, India
2 Department of Biosciences and Biotechnology, Banasthali Vidyapith, Tonk, Rajasthan, India
3 Department of Mathematics and Computing, Banasthali Vidyapith, Tonk, Rajasthan, India
AIH, 025400087 https://doi.org/10.36922/AIH025400087
Received: 5 October 2025 | Revised: 24 November 2025 | Accepted: 1 December 2025 | Published online: 10 December 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Alzheimer’s disease (AD), a progressive neurodegenerative disease, is a major global public health problem. Early and accurate diagnosis is crucial for timely intervention, especially with the prevalence of the condition expected to triple by 2050. Traditional methods have been enhanced by artificial intelligence (AI)–driven techniques, particularly Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). These approaches enhance the early detection and classification of diseases by evaluating complex neuroimaging data. Using Matrix Laboratory, we developed hybrid models integrating CNNs and SVMs to detect AD, focusing on feature extraction, predictive accuracy, and model interpretability for clinical use. While Internet of Things-–based wearable devices are reviewed for their potential in large-scale data processing and real-time monitoring, our empirical work emphasizes practical AI solutions. In conclusion, integration of multimodal neuroimaging data and advanced feature selection techniques holds the potential to enhance diagnostic precision of AD.

Graphical abstract
Keywords
Alzheimer’s disease
Neuroimaging data
Convolutional Neural Network
Support Vector Machine
Matrix laboratory
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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